Data processing systems for orphaned data identification and deletion and related methods

ABSTRACT

In particular embodiments, an Orphaned Data Action System is configured to analyze one or more data systems (e.g., data assets), identify one or more pieces of personal data that are one or more pieces of personal data that are not associated with one or more privacy campaigns of the particular organization, and notify one or more individuals of the particular organization of the one or more pieces of personal data that are one or more pieces of personal data that are not associated with one or more privacy campaigns of the particular organization.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent ApplicationNo. 62/728,435, filed Sep. 7, 2018, and is also a continuation-in-partof U.S. patent application Ser. No. 16/410,566, filed May 13, 2019,which is a continuation-in-part of U.S. patent application Ser. No.16/055,083, filed Aug. 4, 2018, now U.S. Pat. No. 10,289,870, issued May14, 2019, which claims priority from U.S. Provisional Patent ApplicationSer. No. 62/547,530, filed Aug. 18, 2017, and is also acontinuation-in-part of U.S. patent application Ser. No. 15/996,208,filed Jun. 1, 2018, now U.S. Pat. No. 10,181,051, issued Jan. 15, 2019,which claims priority from U.S. Provisional Patent Application Ser. No.62/537,839 filed Jul. 27, 2017, and is also a continuation-in-part ofU.S. patent application Ser. No. 15/853,674, filed Dec. 22, 2017, nowU.S. Pat. No. 10,019,597, issued Jul. 10, 2018, which claims priorityfrom U.S. Provisional Patent Application Ser. No. 62/541,613, filed Aug.4, 2017, and is also a continuation-in-part of U.S. patent applicationSer. No. 15/619,455, filed Jun. 10, 2017, now U.S. Pat. No. 9,851,966,issued Dec. 26, 2017, which is a continuation-in-part of U.S. patentapplication Ser. No. 15/254,901, filed Sep. 1, 2016, now U.S. Pat. No.9,729,583, issued Aug. 8, 2017, which claims priority from: (1) U.S.Provisional Patent Application Ser. No. 62/360,123, filed Jul. 8, 2016;(2) U.S. Provisional Patent Application Ser. No. 62/353,802, filed Jun.23, 2016; and (3) U.S. Provisional Patent Application Ser. No.62/348,695, filed Jun. 10, 2016. The disclosures of all of the abovepatent applications are hereby incorporated herein by reference in theirentirety.

BACKGROUND

Over the past years, privacy and security policies, and relatedoperations have become increasingly important. Breaches in security,leading to the unauthorized access of personal data (which may includesensitive personal data) have become more frequent among companies andother organizations of all sizes. Such personal data may include, but isnot limited to, personally identifiable information (PII), which may beinformation that directly (or indirectly) identifies an individual orentity. Examples of PII include names, addresses, dates of birth, socialsecurity numbers, and biometric identifiers such as a person'sfingerprints or picture. Other personal data may include, for example,customers' Internet browsing habits, purchase history, and even theirpreferences (e.g., likes and dislikes, as provided or obtained throughsocial media).

Many organizations that obtain, use, and transfer personal data,including sensitive personal data, have begun to address these privacyand security issues. To manage personal data, many companies haveattempted to implement operational policies and processes that complywith legal and organizations, or other entities) with certain rightsrelated to the data subject's personal data that is collected, stored,or otherwise processed by an organization. These rights may include, forexample, a right to obtain confirmation of whether a particularorganization is processing their personal data, a right to obtaininformation about the purpose of the processing (e.g., one or morereasons for which the personal data was collected), and other suchrights. Some regulations require organizations to comply with requestsfor such information (e.g., Data Subject Access Requests) withinrelatively short periods of time (e.g., 30 days).

Existing systems for complying with such requests can be inadequate forproducing and providing the required information within the requiredtimelines. This is especially the case for large corporations, which maystore data on several different platforms in differing locations.Accordingly, there is a need for improved systems and methods forcomplying with data subject access requests.

SUMMARY

A computer-implemented data processing method for identifying one ormore pieces of personal data that are not associated with the one ormore privacy campaigns of a particular entity, in particularembodiments, comprises: (1) accessing, by one or more processors, viaone or more computer networks, to one or more data assets of theparticular entity; (2) scanning, by one or more processors, the one ormore data assets to generate a catalog of one or more privacy campaignsand one or more pieces of personal information associated with one ormore individuals; (3) storing, by one or more processors, the generatedcatalog in computer memory; (4) scanning, by one or more processors, oneor more data assets based at least in part on the generated catalog toidentify a first portion of the one or more pieces of personal data thatare one or more pieces of personal data that are not associated with theone or more privacy campaigns; (5) generating, by one or moreprocessors, an indication that the first portion of one or more piecesof personal data that are not associated with the one or more privacycampaigns of the particular entity is to be removed from the one or moredata assets; (6) presenting, by one or more processors, the indicationto one or more individuals associated with the particular entity; and(7) removing, by one or more processors, the first portion of the one ormore pieces of personal data that are not associated with the one ormore privacy campaigns of the particular entity from the one or moredata assets.

A computer-implemented data processing method for removing one or morepieces of personal data that are not associated with the one or moreprivacy campaigns of a particular entity, in particular embodiments,comprises: (1) accessing, by one or more processors, via one or morecomputer networks, one or more data models that map an associationbetween (i) one or more pieces of personal data associated with one ormore individuals stored within one or more data assets of the particularentity and (ii) one or more privacy campaigns of the particular entity;(2) analyzing, by one or more processors, the one or more data models toidentify a first portion of the one or more pieces of personal data thatare one or more pieces of personal data that are not associated with theone or more privacy campaigns; and (3) automatically removing the firstportion of the one or more pieces of personal data that are notassociated with the one or more privacy campaigns of the particularentity from the one or more data assets.

Various embodiments are also described in the following listing ofconcepts:

1. A computer-implemented data processing method for identifying one ormore pieces of personal data that are not associated with one or moreprivacy campaigns of a particular entity, the method comprising:

accessing, by one or more processors, via one or more computer networks,one or more data assets of the particular entity;

scanning, by one or more processors, the one or more data assets togenerate a catalog of one or more privacy campaigns and one or morepieces of personal information associated with one or more individuals;

storing, by one or more processors, the generated catalog in computermemory; scanning, by one or more processors, one or more data assetsbased at least in part on the generated catalog to identify a firstportion of the one or more pieces of personal data that are one or morepieces of personal data that are not associated with the one or moreprivacy campaigns;

generating, by one or more processors, an indication that the firstportion of one or more pieces of personal data that are not associatedwith the one or more privacy campaigns of the particular entity is to beremoved from the one or more data assets;

presenting, by one or more processors, the indication to one or moreindividuals associated with the particular entity; and

removing, by one or more processors, the first portion of the one ormore pieces of personal data that are not associated with the one ormore privacy campaigns of the particular entity from the one or moredata assets.

2. The computer-implemented data processing method of Concept 1, whereinthe first portion of the one or more pieces of personal data that arenot associated with the one or more privacy campaigns of the particularentity are automatically removed from the one or more data assets.

3. The computer-implemented data processing method of Concept 1, furthercomprising:

determining that one or more privacy campaigns have been terminatedwithin the one or more data assets of the particular entity;

scanning the one or more data assets based at least in part on thegenerated catalog to identify the one or more pieces of personal datathat are associated with the terminated one or more privacy campaigns;and

generating an indication that the one or more pieces of personal datathat are associated with the terminated one or more privacy campaignsare included in the first portion of the one or more pieces of personaldata.

4. The computer-implemented data processing method of Concept 3, furthercomprising:

determining that one or more privacy campaigns of the particular entityhave not been utilized in a period of time; and

terminating the one or more privacy campaigns of the particular entitythat have not been utilized in the period of time.

5. The computer-implemented data processing method of Concept 4, whereinthe period of time is ninety or more days.

6. The computer-implemented data processing method of Concept 1, whereinpresenting the indication to the one or more individuals associated withthe particular entity further comprises:

-   -   receiving, by one or more processors, a selection, by the one or        more individuals associated with the particular entity, of a        first set of the one or more pieces of personal data of the        first portion of the one or more pieces of personal data to        retain based on one or more bases to retain the first set of the        one or more pieces of personal data;

prompting, by one or more processors, the one or more individuals toprovide one or more bases to retain the first set of the one or morepieces of personal data of the first portion of the one or more piecesof personal data that are not associated with the one or more privacycampaigns;

receiving, by one or more processors, the provided one or more bases toretain the first set of the one or more pieces of personal data of thefirst portion of the one or more pieces of personal data from the one ormore individuals associated with the particular entity;

retaining, by one or more processors, the first set of the one or morepieces of personal data of the first portion of the one or more piecesof personal data from the one or more individuals associated with theparticular entity; and

removing a second set of the one or more pieces of personal data of thefirst portion of the one or more pieces of personal data that are notassociated with the one or more privacy campaigns from the one or moredata assets, wherein the second set of the one or more pieces ofpersonal data is different from the first set of the one or more piecesof personal data and the first portion of the one or more pieces ofpersonal data comprise the first set of the one or more pieces ofpersonal data and the second set of the one or more pieces of personaldata.

7. The computer-implemented data processing method of Concept 6, furthercomprising:

in response to receiving the provided one or more bases to retain thefirst set of the one or more pieces of personal data from the one ormore individuals associated with the particular entity, submitting theprovided one or more bases to retain the first set of the one or morepieces of personal data to one or more second individuals associatedwith the particular entity for authorization.

8. The computer-implemented data processing method of Concept 6, whereinthe second set of the one or more pieces of personal data does notinclude one or more pieces of personal data.

9. A computer-implemented data processing method for removing one ormore pieces of personal data that are not associated with one or moreprivacy campaigns of a particular entity, the method comprising:

accessing, by one or more processors, via one or more computer networks,one or more data models that map an association between (i) one or morepieces of personal data associated with one or more individuals storedwithin one or more data assets of the particular entity and (ii) one ormore privacy campaigns of the particular entity;

analyzing, by one or more processors, the one or more data models toidentify a first portion of the one or more pieces of personal data thatare one or more pieces of personal data that are not associated with theone or more privacy campaigns; and

automatically removing the first portion of the one or more pieces ofpersonal data that are not associated with the one or more privacycampaigns of the particular entity from the one or more data assets.

10. The computer-implemented data processing method of Concept 9,further comprising:

receiving, by one or more processors, an indication of a new privacycampaign initiated by the particular entity;

in response to receiving the indication of the new privacy campaigninitiated by the particular entity, modifying the one or more datamodels to map an association between (i) one or more pieces of personaldata associated with one or more individuals obtained in connection withthe new privacy campaign and (ii) the new privacy campaign initiated bythe particular entity.

11. The computer-implemented data processing method of Concept 9,further comprising:

generating an indication that the first portion of the one or morepieces of personal data that are not associated with the one or moreprivacy campaigns of the particular entity is to be removed from the oneor more data assets of the particular entity; and presenting theindication to one or more individuals associated with the particularentity.

12. The computer-implemented data processing method of Concept 9,further comprising:

determining that one or more privacy campaigns have been terminatedwithin the one or more data assets of the particular entity;

analyzing, by one or more processors, the one or more data models toidentify one or more pieces of personal data that are one or more piecesof personal data that are associated with the terminated one or moreprivacy campaigns; and

generating an indication that the one or more pieces of personal datathat are associated with the terminated one or more privacy campaignsare included in the first portion of the one or more pieces of personaldata.

13. The computer-implemented data processing method of Concept 12,further comprising:

determining that one or more privacy campaigns of the particular entityhave not been utilized in a period of time; and

terminating the one or more privacy campaigns of the particular entityhave not been utilized in the period of time.

14. The computer-implemented data processing method of Concept 13,wherein the period of time is ninety or more days.

15. A computer-implemented data processing method for generating aprivacy data report of a particular entity, the method comprising:

accessing, by one or more processors, via one or more computer networks,one or more data models that map an association between (i) one or morepieces of personal information of one or more individuals stored withinone or more data assets of the particular entity and (ii) one or moreprivacy campaigns of the particular entity;

accessing, by one or more processors,

-   -   a data collection policy of the particular entity that is based        at least in part on one or more collection parameters defining        how one or more pieces of personal data of one or more        individuals is collected by the particular entity and one or        more storage parameters associated with storing the one or more        pieces of personal data of the one or more individuals, and    -   one or more data retention metrics of the particular entity that        are based at least in part on the collection and storage by the        particular entity of the one or more pieces of personal data of        one or more individuals;

analyzing, by or more processors, the one or more data models toidentify one or more pieces of personal data that are not associatedwith the one or more privacy campaigns;

generating, by one or more processors, a privacy data report based atleast in part on (i) analyzing the one or more data models to identifyone or more pieces of personal data that are not associated with the oneor more privacy campaigns, (ii) the data collection policy of theparticular entity, and (iii) the one or more data retention metrics ofthe particular entity; and

providing, by one or more processors, the privacy data report to one ormore individuals associated with the particular entity.

16. The computer-implemented data processing method of Concept 15,wherein the privacy data report comprises a comparison of the datacollection policy and the one or more data retention metrics of theparticular entity to one or more industry standard data collectionpolicies and one or more industry standard data retention metrics.

17. The computer-implemented data processing method of Concept 15,wherein generating the privacy data report further comprises:

calculating a data risk score for the particular entity based at leastin part on (i) analyzing the one or more data models to identify one ormore pieces of personal data that are not associated with the one ormore privacy campaigns, (ii) the data collection policy of theparticular entity, and (iii) the one or more data retention metrics ofthe particular entity.

18. The computer-implemented data processing method of Concept 17,further comprising:

comparing the data risk score for the particular entity to a thresholddata risk score;

determining that the data risk score for the particular entity is lessthan the threshold data risk score;

in response to determining that the data risk score for the particularentity is less than the threshold risk score, generating a notificationto indicate that the data risk score for the particular entity is lessthan the threshold risk score; and

providing the notification to the one or more individuals associatedwith the particular entity.

19. The computer-implemented data processing method of Concept 17,further comprising:

comparing the data risk score for the particular entity to a thresholddata risk score;

determining that the data risk score for the particular entity isgreater than or equal to the threshold data risk score;

in response to determining that the data risk score for the particularentity is greater than the threshold risk score, generating anotification to indicate that the data risk score for the particularentity is greater than the threshold risk score; and

providing the notification to the one or more individuals associatedwith the particular entity.

20. The computer-implemented data processing method of Concept 15,wherein the one or more data retention metrics comprise at least onedata retention metric selected from a group consisting of:

a storage location of the one or more pieces of personal data;

a period of time the one or more pieces of personal data are stored bythe particular entity;

a number of the one or more privacy campaigns accessing the one or morepieces of personal data; and

an amount of the one or more pieces of personal data being collected bythe particular entity.

A computer-implemented data processing method for generating a privacydata report of a particular entity, in particular embodiments,comprises: (1) accessing, by one or more processors, via one or morecomputer networks, one or more data models that map an associationbetween (i) one or more pieces of personal information of one or moreindividuals stored within one or more data assets of the particularentity and (ii) one or more privacy campaigns of the particular entity;(2) accessing, by one or more processors, (i) a data collection policyof the particular entity that based at least in part on one or morecollection parameters defining how one or more pieces of personal dataof one or more individuals is collected by the particular entity and oneor more storage parameters associated with storing the one or morepieces of personal data of the one or more individuals, and (ii) one ormore data retention metrics of the particular entity that are based atleast in part on the collection and storage by the particular entity ofthe one or more pieces of personal data of one or more individuals; (3)analyzing, by or more processors, the one or more data models toidentify one or more pieces of personal data that are not associatedwith the one or more privacy campaigns; (4) generating, by one or moreprocessors, a privacy data report based at least in part on (i)analyzing the one or more data models to identify one or more pieces ofpersonal data that are not associated with the one or more privacycampaigns, (ii) the data collection policy of the particular entity, and(iii) the one or more data retention metrics of the particular entity;and (5) providing, by one or more processors, the privacy data report toone or more individuals associated with the particular entity.

A data management computer system for confirming a deletion of personaldata associated with a data subject from one or more computer systemsassociated with an entity, in particular embodiments, comprises: (1) oneor more computer processors; and (2) computer memory operatively coupledto the one or more processors, wherein the one or more computerprocessors are adapted for: (a) receiving an indication that the entityhas completed an erasure of one or more pieces of personal dataassociated with the data subject under a right of erasure; (b) inresponse to receiving the indication that the entity (e.g., one or morecomputer systems associated with the entity) has completed the erasure,initiating a test interaction between a test data subject and theentity, the test interaction requiring a response from the entity to thetest data subject; (c) in response to initiating the test interaction,determining whether one or more system associated with the entity havetransmitted the response to the test data subject; and (d) in responseto determining that the one or more systems associated with the entityhave transmitted the response, (i) determining that the entity has notcompleted the erasure of the one or more pieces of personal dataassociated with the test data subject, and (ii) automatically taking oneor more actions with regard to the personal data associated with thetest data subject.

A data management computer system for confirming a deletion of personaldata associated with a data subject from one or more computer systemsassociated with an entity, in particular embodiments, comprises: (1) oneor more computer processors; and (2) computer memory operatively coupledto the one or more processors, wherein the one or more computerprocessors are adapted for: (a) receiving an indication that the entityhas completed an erasure of one or more pieces of personal dataassociated with a test data subject under a right of erasure; (b) inresponse to receiving the indication that the entity has completed theerasure, initiating a test interaction between a test data subject andthe entity, the test interaction requiring a response from the entity tothe test data subject; (c) in response to initiating the testinteraction, determining whether one or more system associated with theentity have initiated a test interaction response to the data subjectbased at least in part on the test interaction; and (d) in response todetermining that the one or more systems associated with the entity haveinitiated the test interaction response, (i) determining that the entityhas not completed the erasure of the one or more pieces of personal dataassociated with the data subject, and (ii) automatically taking one ormore actions with regard to the personal data associated with the datasubject.

A computer-implemented data processing method, in particularembodiments, comprises: (1) providing a communication to the entity,wherein the communication, (a) comprises a unique identifier associatedwith the data subject, (b) is performed without using a personalcommunication data platform, and (c) prompts the entity to provide aresponse by contacting the data subject via a personal communicationdata platform; (2) in response to providing the communication to theentity, determining whether the data subject has received a response viathe personal communication data platform; (3) in response to determiningthat the data subject has received the response via the personalcommunication data platform, determining that the entity has notcomplied with the data subject's request for deletion of their personaldata by the entity; (4) in response to determining that the entity hasnot complied with the data subject's request for deletion, generating anindication that the entity has not complied with the data subject'srequest for deletion of their personal data by the entity; and (5)digitally storing the indication that the entity has not complied withthe data subject's request for deletion of their personal data incomputer memory.

Various embodiments are also described in the following listing ofconcepts:

1. A data management computer system for confirming a deletion ofpersonal data associated with a data subject from one or more computersystems associated with an entity, the system comprising:

-   -   one or more computer processors; and    -   computer memory operatively coupled to the one or more        processors, wherein the one or more computer processors are        adapted for:        -   receiving an indication that the one or more computer            systems have completed an erasure of one or more pieces of            personal data associated with the data subject;        -   in response to receiving the indication that the one or more            computer systems have completed the erasure, initiating a            test interaction between the data subject and the entity,            the test interaction requiring a response from the entity to            the data subject;        -   in response to initiating the test interaction, determining            whether one or more computer systems associated with the            entity have initiated a test interaction response to the            data subject based at least in part on the test interaction;            and        -   in response to determining that the one or more computer            systems associated with the entity have initiated the test            interaction response:            -   determining whether the one or more computer systems                have completed the erasure of the one or more pieces of                personal data associated with the data subject; and            -   automatically taking one or more actions with regard to                the personal data associated with the data subject.

2. The data management computer system of Concept 1, wherein the one ormore actions comprise:

identifying the one or more pieces of personal data associated with thedata subject that remain stored in the one or more computer systems ofthe entity;

flagging the one or more pieces of personal data associated with thedata subject that remain stored in the one or more computer systems ofthe entity; and

providing the flagged one or more pieces of personal data associatedwith the data subject that remain stored in the one or more computersystems of the entity to an individual associated with the entity.

3. The data management computer system of Concept 1, wherein:

initiating the test interaction between the data subject and the entitycomprises substantially automatically completing a contact-request formhosted by the entity on behalf of the data subject.

4. The data management computer system of Concept 3, wherein:

substantially automatically completing the contact-request formcomprises providing one or more pieces of identifying data associatedwith the data subject, the one or more pieces of identifying datacomprising data other than contact data.

5. The data management computer system of Concept 4, wherein determiningwhether the one or more system associated with the entity have generatedthe test interaction response, further comprises: determining whetherthe one or more computer systems of the entity have attempted to contactthe data subject in response to submission of the contact-request form.

6. The data management computer system of Concept 1, wherein the methodfurther comprises initiating a test interaction between the data subjectand the entity in response to determining that a certain period of timehas elapsed from a time that the data subject provided the request todelete the data subject's personal data.

7. The data management computer system of Concept 6, wherein the testinteraction is automatically initiated by the computer system.

8. The data management computer system of Concept 1, wherein the one ormore actions comprise:

generating a report indicating that one or more pieces of personal dataassociated with the data subject remain stored in the one or morecomputer systems of the entity; and providing the report to anindividual associated with the entity.

9. A data management computer system for confirming deletion of personaldata within one or more computer systems associated with an entity, thesystem comprising:

-   -   one or more computer processors; and    -   computer memory operatively coupled to the one or more        processors, wherein the one or more computer processors are        adapted for:        -   receiving an indication that the one or more computer            systems have completed an erasure of one or more pieces of            personal data associated with a test data subject;        -   in response to receiving the indication that the one or more            computer systems have completed the erasure, initiating a            test interaction between a test data subject and the entity,            the test interaction requiring a response from the entity to            the test data subject;        -   in response to initiating the test interaction, determining            whether the one or more computer systems associated with the            entity have transmitted the response to the test data            subject;        -   in response to determining that the one or more computer            systems associated with the entity have transmitted the            response:            -   determining whether the one or more computer systems                have completed the erasure of the one or more pieces of                personal data associated with the test data subject; and            -   automatically taking one or more actions with regard to                the personal data associated with the test data subject.

10. The data management computer system of Concept 9, wherein the one ormore actions comprise:

identifying the one or more pieces of personal data associated with thetest data subject that remain stored in the one or more computer systemsof the entity;

flagging the one or more pieces of personal data associated with thetest data subject that remain stored in the one or more computer systemsof the entity; and

providing the flagged one or more pieces of personal data associatedwith the test data subject that remain stored in the one or morecomputer systems of the entity to an individual associated with theentity.

11. The data management computer system of Concept 9, wherein:

initiating the test interaction between the test data subject and theentity comprises substantially automatically completing acontact-request form hosted by the entity on behalf of the test datasubject.

12. The data management computer system of Concept 11, wherein:

substantially automatically completing the contact-request formcomprises providing one or more pieces of identifying data associatedwith the test data subject, the one or more pieces of identifying datacomprising data other than contact data.

13. The data management computer system of Concept 12, furthercomprising:

determining whether the one or more computer systems associated with theentity have generated the response and transmitted the response to thetest data subject comprises determining whether the one or more computersystems have attempted to contact the test data subject in response tosubmission of the contact-request form.

14. The data management computer system of Concept 13, wherein themethod further comprises initiating a test interaction between the datasubject and the entity in response to determining that a certain periodof time has elapsed from a time that the data subject provided therequest to delete the data subject's personal data.

15. The data management computer system of Concept 14, wherein the testinteraction is automatically initiated by the computer system.

16. The data management computer system of Concept 9, wherein the one ormore actions comprise:

generating a report indicating that one or more pieces of personal dataassociated with the test data subject that remain stored in the one ormore computer systems of the entity; and providing the report to anindividual associated with the entity.

17. A computer-implemented data processing method for monitoringcompliance by a particular entity with a data subject's request todelete the data subject's personal data from one or more computersystems associated with a particular entity, the method comprising:

providing a communication to the entity, wherein the communication:

-   -   (a) comprises a unique identifier associated with the data        subject;    -   (b) is performed without using a personal communication data        platform, and    -   (c) prompts the entity to provide a response by contacting the        data subject via a personal communication data platform;

in response to providing the communication to the entity, determiningwhether the data subject has received a response via the personalcommunication data platform;

in response to determining that the data subject has received theresponse via the personal communication data platform, determiningwhether the one or more computer systems have executed the datasubject's request for deletion of the data subject's personal data;

in response to determining that the one or more computer systems havenot complied with the data subject's request for deletion, generating anindication that the one or more computer systems have not complied withthe data subject's request for deletion of the data subject's personaldata; and

digitally storing in computer memory the indication that the one or morecomputer systems have not complied with the data subject's request fordeletion of the data subject's personal data.

18. The computer-implemented data processing method of Concept 17,further comprising:

identifying one or more pieces of personal data associated with the datasubject that are stored in the one or more computer systems of theentity;

flagging the one or more pieces of personal data associated with thedata subject that are stored in the one or more computer systems of theentity; and

providing the flagged one or more pieces of personal data associatedwith the data subject that are stored in the one or more computersystems of the entity to an individual associated with the entity.

19. The computer-implemented data processing method of Concept 17,further comprising:

generating a report based at least in part on the indication that theentity has not complied with the data subject's request for deletion oftheir personal data in computer memory; and

providing the generated report to an individual associated with theentity.

20. The computer-implemented data processing method of Concept 19,wherein the individual associated with the entity is a privacy officerof the entity.

A computer-implemented method for updating risk remediation data for anentity, in particular embodiments, comprises: (1) accessing riskremediation data for an entity that identifies one or more actions toremediate a risk in response to identifying one or more data assets ofthe entity potentially affected by one or more risk triggers; (2)receiving an indication of an update to the one or more data assets; (3)identifying one or more updated risk triggers for an entity based atleast in part on the update to the one or more data assets; (4)determining, by using one or more data models associated with the riskremediation data, one or more updated actions to remediate the one ormore updated risk triggers; (5) analyzing the one or more updated risktriggers to determine a relevance of the risk posed to the entity by theone or more updated risk triggers; and (6) updating the risk remediationdata to include the one or more updated actions to remediate the risk inresponse to identifying the one or more updated risk triggers.

A computer-implemented method for updating risk remediation data of anentity, in particular embodiments, comprises: (1) receiving anindication of an update to the first data asset of the entity receivingan indication of an update to the first data asset of the entity; (2)identifying one or more risk triggers for an entity based at least inpart on the update to the first data asset of the entity; (3)identifying a second data asset of the entity potentially affected bythe one or more risk triggers based at least in part on an associationof the first data asset and the second data asset; (4) determining, byusing one or more data models, one or more first updated actions toremediate the one or more updated risk triggers for the first dataasset; (5) determining, by using one or more data models, one or moresecond updated actions to remediate the one or more updated risktriggers for the second data asset; and (6) generating risk remediationdata of the entity to include the one or more first updated actions andthe one or more second updated actions to remediate the one or morepotential risk triggers.

A computer-implemented method for generating risk remediation data foran entity, in particular embodiments, comprises: (1) accessing aggregaterisk remediation data for a plurality of identified risk triggers fromone or more organizations; (2) analyzing the aggregate risk remediationdata to determine a remediation outcome for each of the plurality ofidentified risk triggers and an associated entity response to theparticular identified risk trigger of the plurality of identified risktriggers; (3) in response to analyzing the aggregate risk remediationdata to determine a remediation outcome for each of the plurality ofidentified risk triggers and an associated entity response to theparticular identified risk trigger of the plurality of identified risktriggers, generating one or more risk remediation data models; and (4)generating risk remediation data for the entity based at least in parton the one or more risk remediation data models and one or more dataassets of the entity.

Various embodiments are also described in the following listing ofconcepts:

1. A computer-implemented data processing method for updating riskremediation data for an entity, the method comprising:

accessing risk remediation data for an entity that identifies one ormore actions to remediate a risk in response to identifying one or moredata assets of the entity potentially affected by one or more risktriggers;

receiving an indication of an update to the one or more data assets;

identifying one or more updated risk triggers for an entity based atleast in part on the update to the one or more data assets;

determining, by using one or more data models associated with the riskremediation data, one or more updated actions to remediate the one ormore updated risk triggers;

analyzing the one or more updated risk triggers to determine a relevanceof the risk posed to the entity by the one or more updated risktriggers; and

updating the risk remediation data to include the one or more updatedactions to remediate the risk in response to identifying the one or moreupdated risk triggers.

2. The computer-implemented data processing method of Concept 1, furthercomprising:

determining, based at least in part on the one or more data assets andthe relevance of the risk, whether to take one or more updated actionsin response to the one or more updated risk triggers; and

taking the one or more updated actions to remediate the risk in responseto identifying the one or more updated risk triggers.

3. The computer-implemented data processing method of Concept 1, whereinupdating the risk remediation data is performed automatically.

4. The computer-implemented data processing method of Concept 1, whereinthe one or more updated risk triggers comprises the one or more dataassets being physically located in one or more particular locations.

5. The computer-implemented data processing method of Concept 4, whereinthe one or more particular locations comprise a single physicallocation.

6. The computer-implemented data processing method of Concept 1, whereinanalyzing the one or more updated risk triggers to determine therelevance of the risk posed to the entity by the one or more updatedrisk triggers further comprises:

calculating a risk level based at least in part on the one or moreupdated risk triggers;

in response to calculating the risk level, comparing the risk level to athreshold risk level for the entity; and

in response to determining that the risk level is greater than or equalto the threshold risk level, updating the risk remediation data toinclude the one or more updated actions to remediate the risk inresponse to identifying the one or more updated risk triggers.

7. The computer-implemented data processing method of Concept 6, whereincalculating the risk level based at least in part on the one or moreupdated risk triggers further comprises comparing the one or moreupdated risk triggers to (i) one or more previously identified risktriggers, and (ii) one or more previously implemented actions to the oneor more previously identified risk triggers.

8. The computer-implemented data processing method of Concept 1, themethod further comprising generating at least one data model of the oneor more data models by:

receiving aggregate risk remediation data for a plurality of identifiedrisk triggers from one or more organizations;

analyzing the aggregate risk remediation data to determine a remediationoutcome for each of the plurality of identified risk triggers and anassociated entity response to the particular identified risk trigger ofthe plurality of identified risk triggers; and

in response to analyzing the aggregate risk remediation data todetermine a remediation outcome for each of the plurality of identifiedrisk triggers and an associated entity response to the particularidentified risk trigger of the plurality of identified risk triggers,generating the at least one data model of the one or more data models.

9. The computer-implemented data processing method of Concept 8, whereinthe risk remediation data implements the at least one data model of theone or more data models.

10. The computer-implemented data processing method of Concept 8,wherein the one or more organizations comprises the entity.

11. A computer-implemented data processing method for updating riskremediation data of an entity, the method comprising:

receiving an indication of an update to the first data asset of theentity;

identifying one or more risk triggers for an entity based at least inpart on the update to the first data asset of the entity;

identifying a second data asset of the entity potentially affected bythe one or more risk triggers based at least in part on an associationof the first data asset and the second data asset;

determining, by using one or more data models, one or more first updatedactions to remediate the one or more updated risk triggers for the firstdata asset;

determining, by using one or more data models, one or more secondupdated actions to remediate the one or more updated risk triggers forthe second data asset; and

generating risk remediation data of the entity to include the one ormore first updated actions and the one or more second updated actions toremediate the one or more potential risk triggers.

12. The computer-implemented data processing method of Concept 11,further comprising:

determining a first data asset risk level based at least in part on theone or more updated risk triggers for the first data asset;

determining to take the one or more first updated actions to remediatethe one or more updated risk triggers for the first data asset based atleast in part on the first data asset risk level; and

in response, taking the first updated actions to remediate the one ormore updated risk triggers for the first data asset.

13. The computer-implemented data processing method of Concept 12,further comprising:

comparing the first data asset risk level to a threshold data asset risklevel; and

in response to determining that the first data asset risk level isgreater than or equal to the threshold data asset risk level, taking thefirst updated actions to remediate the one or more updated risk triggersfor the first data asset.

14. The computer-implemented data processing method of Concept 11,wherein the one or more first updated actions to remediate the one ormore updated risk triggers for the first data asset is the one or moresecond updated actions to remediate the one or more updated risktriggers for the second data asset.

15. The computer-implemented data processing method of Concept 11,wherein the one or more first updated actions to remediate the one ormore updated risk triggers for the first data asset is different fromthe one or more second updated actions to remediate the one or moreupdated risk triggers for the second data asset.

16. The computer-implemented data processing method of Concept 11,wherein generating the risk remediation data of the entity to includethe one or more first updated actions and the one or more second updatedactions to remediate the one or more potential risk triggers isperformed automatically.

17. The computer-implemented data processing method of Concept 11,wherein the one or more risk triggers comprises one or more of the firstdata asset and the second data asset being physically located in aparticular one or more locations.

18. The computer-implemented data processing method of Concept 17,wherein the one or more risk triggers comprises the first data assetbeing located in a first physical location and the second data assetbeing located in the first physical location.

19. A computer-implemented data processing method for generating riskremediation data for an entity, the method comprising:

accessing aggregate risk remediation data for a plurality of identifiedrisk triggers from one or more organizations;

analyzing the aggregate risk remediation data to determine a remediationoutcome for each of the plurality of identified risk triggers and anassociated entity response to the particular identified risk trigger ofthe plurality of identified risk triggers;

in response to analyzing the aggregate risk remediation data todetermine a remediation outcome for each of the plurality of identifiedrisk triggers and an associated entity response to the particularidentified risk trigger of the plurality of identified risk triggers,generating one or more risk remediation data models; and

generating risk remediation data for the entity based at least in parton the one or more risk remediation models and one or more data assetsof the entity.

20. The computer-implemented data processing method of Concept 19,further comprising updating the generated risk remediation dataautomatically.

A computer-implemented method for managing a plurality of data assets ofan organization with a third-party data repository, in particularembodiments, comprises: (1) identifying a form used to collect one ormore pieces of personal data; (2) determining one or more data assets ofa plurality of data assets of the organization where input data of theform is transmitted; (3) adding the one or more data assets to thethird-party data repository with an electronic link to the form; (4) inresponse to a user submitting the form, creating a unique subjectidentifier associated with the user; (5) transmitting the unique subjectidentifier (i) to the third-party data repository and (ii) along withthe form data provided by the user in the form, to the data asset; and(6) digitally storing the unique subject identifier (i) in thethird-party data repository and (ii) along with the form data providedby the user in the form, in the data asset.

A computer-implemented method for or managing a plurality of data assetsof an organization with a unique subject identifier database, inparticular embodiments, comprises: (1) receiving an indication ofcompletion of a form associated with the organization by a data subject;(2) determining, based at least in part on searching a unique subjectidentifier database, whether a unique subject identifier has beengenerated for the data subject; (3) in response to determining that aunique subject identifier has not been generated for the data subject,generating a unique subject identifier for the data subject; and (4)storing the unique subject identifier for the data subject in the uniquesubject identifier database, wherein the unique subject identifierdatabase electronically links each respective unique subject identifierto each of: (i) the form associated with the organization submitted bythe data subject of each respective unique subject identifier, and (ii)one or more data assets that utilize form data of the form received fromthe data subject.

A computer-implemented method for managing a plurality of data assets ofan organization with a unique subject identifier database that, inparticular embodiments, comprises: (1) receiving an indication ofcompletion of a form associated with the organization by a data subject;(2) determining, based at least in part on searching a unique subjectidentifier database, whether a unique subject identifier has beengenerated for the data subject; (3) in response to determining that aunique subject identifier has been generated for the data subject,accessing the unique subject identifier database; (4) identifying theunique subject identifier of the data subject based at least in part onform data provided by the data subject in the completion of the formassociated with the organization; and (5) updating the unique subjectidentifier database to include an electronic link between the uniquesubject identifier of the data subject and each of (i) the formsubmitted by the data subject of each respective unique subjectidentifier, and (ii) one or more data assets that utilize the form dataof the form received from the data subject.

Various embodiments are also described in the following listing ofconcepts:

1. A computer-implemented data processing method for managing aplurality of data assets of an organization shared with a third-partydata repository, the method comprising:

identifying a form used to collect one or more pieces of personal data;

determining one or more data assets of a plurality of data assets of theorganization where input data of the form is transmitted;

adding the one or more data assets to the third-party data repositorywith an electronic link to the form;

in response to a user submitting the form, creating a unique subjectidentifier associated with the user;

transmitting the unique subject identifier to the third-party datarepository along with the form data provided by the user in the form, tothe data asset; and

digitally storing the unique subject identifier in the third-party datarepository and along with the form data provided by the user in theform, in the data asset.

2. The computer-implemented data processing method of Concept 1, furthercomprising:

receiving a data subject access request from the user;

accessing the third-party data repository to identify the unique subjectidentifier of the user;

determining which one or more data assets of the plurality of dataassets of the organization include the unique subject identifier; and

accessing personal data of the user stored in each of the one or moredata assets of the plurality of data assets of the organization thatinclude the unique subject identifier.

3. The computer-implemented data processing method of Concept 2, whereinthe data subject access request comprises a type of data subject accessrequest, and wherein the type of data subject access request is selectedfrom a group consisting of:

a subject's rights request, and

a data subject deletion request.

4. The computer-implemented data processing method of Concept 3, whereinthe type of data subject access request is a data subject deletionrequest and further comprising:

in response to accessing the personal data of the user stored in each ofthe one or more data assets of the plurality of data assets of theorganization that include the unique subject identifier, deleting thepersonal data of the user stored in each of the one or more data assetsof the plurality of data assets of the organization that include theunique subject identifier.

5. The computer-implemented data processing method of Concept 3, whereinthe type of data subject access request is a data subject deletionrequest and the method further comprises:

in response to accessing the personal data of the user stored in each ofthe one or more data assets of the plurality of data assets,automatically determining that a first portion of personal data of theuser stored in the one or more data assets has one or more legal basesfor continued storage;

in response to determining that the first portion of personal data ofthe user stored in the one or more data assets has one or more legalbases for continued storage, automatically maintaining storage of thefirst portion of personal data of the user stored in the one or moredata assets;

automatically facilitating deletion of a second portion of personal dataof the user stored in the one or more data assets for which one or morelegal bases for continued storage cannot be determined, wherein thefirst portion of the personal data of the user stored in the one or moredata assets is different from the second portion of personal data of theuser stored in the one or more data assets; and

automatically marking as free one or more memory addresses associatedwith the second portion of personal data of the user stored in the oneor more data assets associated with the user.

6. The computer-implemented data processing method of Concept 1, whereinidentifying a form used to collect one or more pieces of personal datais performed by using one or more website scanning tools.

7. The computer-implemented data processing method of Concept 1, whereinthe third-party data repository comprises a link to each of the one ormore data assets of the plurality of data assets of the organizationthat include the unique subject identifier of the user.

8. The computer-implemented data processing of Concept 1, wherein thethird-party data repository stores the unique subject identifier in adatabase of a plurality of unique subject identifiers.

9. A computer-implemented data processing method for managing aplurality of data assets of an organization with a unique subjectidentifier database, the method comprising:

receiving an indication of completion of a form associated with theorganization by a data subject;

determining, based at least in part on searching a unique subjectidentifier database, whether a unique subject identifier has beengenerated for the data subject;

in response to determining that a unique subject identifier has not beengenerated for the data subject, generating a unique subject identifierfor the data subject; and

storing the unique subject identifier for the data subject in the uniquesubject identifier database, wherein the unique subject identifierdatabase electronically links each respective unique subject identifierto each of: (i) the form associated with the organization submitted bythe data subject of each respective unique subject identifier, and (ii)one or more data assets that utilize form data of the form received fromthe data subject.

10. The computer-implemented data processing method of Concept 9,further comprising:

receiving a data subject access request from the data subject;

accessing the unique subject identifier database to identify the uniquesubject identifier of the data subject;

determining which one or more data assets of the plurality of dataassets of the organization include the unique subject identifier of thedata subject; and

accessing personal data of the data subject stored in each of the one ormore data assets of the plurality of data assets of the organizationthat include the unique subject identifier.

11. The computer-implemented data processing method of Concept 10,wherein the data subject access request comprises a type of data subjectaccess request, and wherein the type of data subject access request isselected from a group consisting of:

a subject's rights request, and

a data subject deletion request.

12. The computer-implemented data processing method of Concept 11,wherein the type of data subject access request is a data subjectdeletion request and further comprising:

in response to accessing the personal data of the data subject stored ineach of the one or more data assets of the plurality of data assets ofthe organization that include the unique subject identifier, deletingthe personal data of the data subject stored in each of the one or moredata assets of the plurality of data assets of the organization thatinclude the unique subject identifier.

13. The computer-implemented data processing method of Concept 9,further comprising:

in response to determining that a unique subject identifier has beengenerated for the data subject, accessing the unique subject identifierdatabase; and

identifying the unique subject identifier of the data subject based atleast in part on form data provided by the data subject in thecompletion of the form associated with the organization.

14. The computer-implemented data processing method of Concept 13,further comprising:

updating the unique subject identifier database to include an electroniclink between the unique subject identifier of the data subject and eachof (i) the form submitted by the data subject of each respective uniquesubject identifier, and (ii) one or more data assets that utilize theform data of the form received from the data subject.

15. A computer-implemented data processing method for managing aplurality of data assets of an organization with a unique subjectidentifier database, the method comprising:

receiving an indication of completion of a form associated with theorganization by a data subject;

determining, based at least in part on searching a unique subjectidentifier database, whether a unique subject identifier has beengenerated for the data subject;

in response to determining that a unique subject identifier has beengenerated for the data subject, accessing the unique subject identifierdatabase;

identifying the unique subject identifier of the data subject based atleast in part on form data provided by the data subject in thecompletion of the form associated with the organization; and

updating the unique subject identifier database to include an electroniclink between the unique subject identifier of the data subject and eachof (i) the form submitted by the data subject of each respective uniquesubject identifier, and (ii) one or more data assets that utilize theform data of the form received from the data subject.

16. The computer-implemented data processing method of Concept 15,further comprising:

receiving a data subject access request from the data subject;

accessing the unique subject identifier database to identify the uniquesubject identifier of the data subject;

determining which one or more data assets of the plurality of dataassets of the organization include the unique subject identifier of thedata subject; and

accessing personal data of the data subject stored in each of the one ormore data assets of the plurality of data assets of the organizationthat include the unique subject identifier.

17. The computer-implemented data processing method of Concept 16,wherein the data subject access request comprises a type of data subjectaccess request, and wherein the type of data subject access request isselected from a group consisting of:

a subject's rights request, and

a data subject deletion request.

18. The computer-implemented data processing method of Concept 17,wherein the type of data subject access request is a data subjectdeletion request and further comprising:

in response to accessing the personal data of the data subject stored ineach of the one or more data assets of the plurality of data assets ofthe organization that include the unique subject identifier, deletingthe personal data of the data subject stored in each of the one or moredata assets of the plurality of data assets of the organization thatinclude the unique subject identifier.

19. The computer-implemented data processing method of Concept 17,wherein the type of data subject access request is a data subjectdeletion request and the method further comprises:

in response to accessing the personal data of the data subject stored ineach of the one or more data assets of the plurality of data assets,automatically determining that a first portion of personal data of thedata subject stored in the one or more data assets has one or more legalbases for continued storage;

in response to determining that the first portion of personal data ofthe data subject stored in the one or more data assets has one or morelegal bases for continued storage, automatically maintaining storage ofthe first portion of personal data of the data subject stored in the oneor more data assets;

automatically facilitating deletion of a second portion of personal dataof the data subject stored in the one or more data assets for which oneor more legal bases for continued storage cannot be determined, whereinthe first portion of the personal data of the data subject stored in theone or more data assets is different from the second portion of personaldata of the data subject stored in the one or more data assets; and

automatically marking one or more memory addresses associated with thesecond portion of personal data of the data subject stored in the one ormore data assets associated with the data subject as free.

20. The computer-implemented data processing of Concept 1, wherein theunique subject identifier database is a part of a third-party datarepository.

A computer-implemented method for assessing a risk associated with oneor more data transfers between one or more data assets (e.g., two ormore data assets), in particular embodiments, comprises: (1) creating adata transfer record for a data transfer between a first asset in afirst location and a second asset in a second location; (2) accessing aset of data transfer rules that are associated with the data transferrecord; (3) performing a data transfer assessment based at least in parton applying the set of data transfer rules on the data transfer record;(4) identifying one or more data transfer risks associated with the datatransfer record, based at least in part on the data transfer assessment;(5) calculating a risk score for the data transfer based at least inpart on the one or more data transfer risks associated with the datatransfer record; and (6) digitally storing the risk score for the datatransfer.

A computer-implemented method for assessing a risk associated with oneor more data transfers between one or more data assets, in particularembodiments, comprises: (1) accessing a data transfer record for a datatransfer between a first asset in a first location and a second asset ina second location; (2) accessing a set of data transfer rules that areassociated with the data transfer record, wherein the set of datatransfer rules comprise (a) one or more privacy law framework of the oneor more of the first location and the second location, and (b) one ormore entity framework of one or more of (i) an entity associated withthe one or more first data asset and (ii) an entity associated with theone or more second data asset; (3) performing a data transfer assessmentbased at least in part on applying the set of data transfer rules on thedata transfer record; (4) identifying one or more data transfer risksassociated with the data transfer record, based at least in part on thedata transfer assessment; (5) calculating a risk score for the datatransfer based at least in part on the one or more data transfer risksassociated with the data transfer record; and (6) digitally storing therisk score for the data transfer.

A computer-implemented method for assessing a risk associated with oneor more data transfers between one or more data assets, in particularembodiments, comprises: (1) accessing a data transfer record for a datatransfer between a first asset in a first location and a second asset ina second location; (2) accessing a set of data transfer rules that areassociated with the data transfer record; (3) performing a data transferassessment based at least in part on applying the set of data transferrules on the data transfer record; (4) identifying one or more datatransfer risks associated with the data transfer record, based at leastin part on the data transfer assessment; (5) calculating a risk scorefor the data transfer based at least in part on the one or more datatransfer risks associated with the data transfer record; (6) digitallystoring the risk score for the data transfer; (7) comparing the riskscore for the data transfer to a threshold risk score; (8) determiningthat the risk score for the data transfer is a greater risk than thethreshold risk score; and (9) in response to determining that the riskscore for the data transfer is a greater risk than the threshold riskscore, taking one or more action.

Various embodiments are also described in the following listing ofconcepts:

1. A computer-implemented data processing method for assessing a riskassociated with one or more data transfers between one or more dataassets, the method comprising:

creating a data transfer record for a data transfer between a firstasset in a first location and a second asset in a second location;

accessing a set of data transfer rules that are associated with the datatransfer record;

performing a data transfer assessment based at least in part on applyingthe set of data transfer rules on the data transfer record;

identifying one or more data transfer risks associated with the datatransfer record, based at least in part on the data transfer assessment;

calculating a risk score for the data transfer based at least in part onthe one or more data transfer risks associated with the data transferrecord; and

digitally storing the risk score for the data transfer.

2. The computer-implemented data processing method of Concept 1, whereinthe method further comprises:

comparing the risk score for the data transfer to a threshold riskscore;

determining that the risk score for the data transfer is a greater riskthan the threshold risk score; and

in response to determining that the risk score for the data transfer isa greater risk than the threshold risk score, taking one or more action.

3. The computer-implemented data processing method of Concept 2, whereinthe one or more action is selected from a group consisting of:

providing the data transfer record to one or more individuals for reviewof the data transfer record; and

automatically terminating the data transfer.

4. The computer-implemented data processing method of Concept 2, whereinthe one or more action comprises:

generating a secure link between one or more processors associated withthe first asset in the first location and one or more processorsassociated with the second asset in the second location; and

providing the data transfer via the secure link between the one or moreprocessors associated with the first asset in the first location and theone or more processors associated with the second asset in the secondlocation.

5. The computer-implemented data processing method of Concept 1, whereincalculating a risk score for the data transfer based at least in part onthe one or more data transfer risks associated with the data transferrecord further comprises:

determining a weighting factor for each of the one or more data transferrisks;

determining a risk rating for each of the one or more data transferrisks; and

calculating the risk level for the data transfer based upon, for eachrespective one of the one or more data transfer risks, the risk ratingfor the respective data transfer risk and the weighting factor for therespective data transfer risk.

6. The computer-implemented data processing method of Concept 1, whereinthe one or more data transfer risks are selected from a group consistingof:

a source location of the first location of the one or more first dataasset of the data transfer;

a destination location of the second location of the one or more seconddata asset of the data transfer;

one or more type of data being transferred as part of the data transfer;

a time of the data transfer; and

an amount of data being transferred as part of the data transfer.

7. The computer-implemented data processing method of Concept 1, whereinthe set of data transfer rules are automatically updated.

8. The computer-implemented data processing method of Concept 1, whereinthe set of data transfer rules comprise:

one or more privacy law framework of the one or more of the firstlocation and the second location; and

one or more entity framework of one or more of (i) an entity associatedwith the one or more first data asset and (ii) an entity associated withthe one or more second data asset.

9. A computer-implemented data processing method for assessing a riskassociated with one or more data transfers between one or more dataassets, the method comprising:

accessing a data transfer record for a data transfer between a firstasset in a first location and a second asset in a second location;

accessing a set of data transfer rules that are associated with the datatransfer record, wherein the set of data transfer rules comprise:

-   -   one or more privacy law framework of the one or more of the        first location and the second location, and    -   one or more entity framework of one or more of (i) an entity        associated with the one or more first data asset and (ii) an        entity associated with the one or more second data asset;

performing a data transfer assessment based at least in part on applyingthe set of data transfer rules on the data transfer record;

identifying one or more data transfer risks associated with the datatransfer record, based at least in part on the data transfer assessment;

calculating a risk score for the data transfer based at least in part onthe one or more data transfer risks associated with the data transferrecord; and

digitally storing the risk score for the data transfer.

10. The computer-implemented data processing method of Concept 9,wherein the method further comprises:

comparing the risk score for the data transfer to a threshold riskscore;

determining that the risk score for the data transfer is a greater riskthan the threshold risk score; and

in response to determining that the risk score for the data transfer isa greater risk than the threshold risk score, taking one or more action.

11. The computer-implemented data processing method of Concept 10,wherein the one or more action is selected from a group consisting of:

providing the data transfer record to one or more individuals for reviewof the data transfer record; and

automatically terminating the data transfer.

12. The computer-implemented data processing method of Concept 10,wherein the one or more action comprises:

generating a secure link between one or more processors associated withthe first asset in the first location and one or more processorsassociated with the second asset in the second location; and

providing the data transfer via the secure link between the one or moreprocessors associated with the first asset in the first location and theone or more processors associated with the second asset in the secondlocation.

13. The computer-implemented data processing method of Concept 9,wherein calculating a risk score for the data transfer based at least inpart on the one or more data transfer risks associated with the datatransfer record further comprises:

determining a weighting factor for each of the one or more data transferrisks;

determining a risk rating for each of the one or more data transferrisks; and

calculating the risk level for the data transfer based upon, for eachrespective one of the one or more data transfer risks, the risk ratingfor the respective data transfer risk and the weighting factor for therespective data transfer risk.

14. The computer-implemented data processing method of Concept 9,wherein the one or more data transfer risks are selected from a groupconsisting of:

a source location of the first location of the one or more first dataasset of the data transfer;

a destination location of the second location of the one or more seconddata asset of the data transfer;

one or more type of data being transferred as part of the data transfer;

a time of the data transfer; and

an amount of data being transferred as part of the data transfer.

15. The computer-implemented data processing method of Concept 9,wherein the set of data transfer rules are automatically updated.

16. A computer-implemented data processing method for assessing a riskassociated with one or more data transfers between one or more dataassets, the method comprising:

accessing a data transfer record for a data transfer between a firstasset in a first location and a second asset in a second location;

accessing a set of data transfer rules that are associated with the datatransfer record;

performing a data transfer assessment based at least in part on applyingthe set of data transfer rules on the data transfer record;

identifying one or more data transfer risks associated with the datatransfer record, based at least in part on the data transfer assessment;

calculating a risk score for the data transfer based at least in part onthe one or more data transfer risks associated with the data transferrecord;

digitally storing the risk score for the data transfer;

comparing the risk score for the data transfer to a threshold riskscore;

determining that the risk score for the data transfer is a greater riskthan the threshold risk score; and

in response to determining that the risk score for the data transfer isa greater risk than the threshold risk score, taking one or more action.

17. The computer-implemented data processing method of Concept 16,wherein the one or more action is selected from a group consisting of:

providing the data transfer record to one or more individuals for reviewof the data transfer record; and

automatically terminating the data transfer.

18. The computer-implemented data processing method of Concept 16,wherein the one or more data transfer risks are selected from a groupconsisting of:

a source location of the first location of the one or more first dataasset of the data transfer;

a destination location of the second location of the one or more seconddata asset of the data transfer;

one or more type of data being transferred as part of the data transfer;

a time of the data transfer; and

an amount of data being transferred as part of the data transfer.

19. The computer-implemented data processing method of Concept 16,wherein the one or more action comprises:

generating a secure link between one or more processors associated withthe first asset in the first location and one or more processorsassociated with the second asset in the second location; and

providing the data transfer via the secure link between the one or moreprocessors associated with the first asset in the first location and theone or more processors associated with the second asset in the secondlocation.

20. The computer-implemented data processing method of Concept 16,further comprising:

transferring the data between the first asset in the first location andthe second asset in the second location.

A computer-implemented data processing method for automaticallyclassifying personal information in an electronic document andgenerating a sensitivity score for the electronic document based on theclassification, in particular embodiments, comprises: (1) receiving, byone or more processors, the electronic document for analysis; (2) usingone or more natural language processing techniques, by one or moreprocessors, to decompose data from the electronic document into (i) oneor more structured objects and (ii) one or more values for each of theone or more structured objects; (3) classifying, by one or moreprocessors, each of the one or more structured objects in the electronicdocument based on one or more attributes of the one or more structuredobjects; (4) categorizing, by one or more processors, each of the one ormore structured objects based on a sensitivity of the one or morestructured objects; (5) rating, by one or more processors, the accuracyof the categorization; and (6) generating, by one or more processors, asensitivity score for the electronic document based at least in part onthe categorized one or more structured objects and the associated one ormore values.

A computer-implemented data processing method for automaticallyclassifying personal information in an electronic document andgenerating a sensitivity score for the electronic document based on theclassification, in particular embodiments, comprises: (1) receiving, byone or more processors, the electronic document for analysis; (2)sorting, using one or more natural language processing techniques, datafrom the electronic document into (i) one or more structured objects and(ii) one or more values for each of the one or more structured objects;(3) classifying, by one or more processors, each of the one or morestructured objects in the electronic document based on one or moreattributes of the one or more structured objects; (4) categorizing, byone or more processors, each of the one or more structured objects basedon a sensitivity of the one or more structured objects; (5) generating,by one or more processors, a sensitivity score for the electronicdocument based at least in part on the categorized one or morestructured objects and the associated one or more values; (6) parsingthe classification of one or more structured objects; (7) identifyingeach of the one or more structured objects having an empty associatedvalue; and (8) modifying the classification of one or more structuredobjects to remove the identified one or more structured objects from theclassification.

A computer-implemented data processing method for automaticallyclassifying personal information in an electronic document andgenerating a sensitivity score for the electronic document based on theclassification, in particular embodiments, comprises: (1) receiving, byone or more processors, the electronic document for analysis; (2) usingone or more natural language processing techniques, by one or moreprocessors, to decompose data from the electronic document into (i) oneor more structured objects and (ii) one or more values for each of theone or more structured objects; (3) classifying, by one or moreprocessors, each of the one or more structured objects in the electronicdocument based on one or more attributes of the one or more structuredobjects; (4) categorizing, by one or more processors, each of the one ormore structured objects based on a sensitivity of the one or morestructured objects; and (5) generating, by one or more processors, asensitivity score for the electronic document based at least in part onthe categorized one or more structured objects and the associated one ormore values.

Various embodiments are also described in the following listing ofconcepts:

1. A computer-implemented data processing method for automaticallyclassifying personal information in an electronic document andgenerating a sensitivity score for the electronic document based on theclassification, the method comprising:

receiving, by one or more processors, the electronic document foranalysis;

using one or more natural language processing techniques, by one or moreprocessors, to decompose data from the electronic document into:

-   -   one or more structured objects; and    -   one or more values for each of the one or more structured        objects;

classifying, by one or more processors, each of the one or morestructured objects in the electronic document based on one or moreattributes of the one or more structured objects;

categorizing, by one or more processors, each of the one or morestructured objects based on a sensitivity of the one or more structuredobjects;

rating, by one or more processors, the accuracy of the categorization;and

generating, by one or more processors, a sensitivity score for theelectronic document based at least in part on the categorized one ormore structured objects and the associated one or more values.

2. The computer-implemented data processing method of Concept 1, whereingenerating the sensitivity score for the electronic document comprises:

assigning a relative sensitivity rating to each of the one or morestructured objects; and

calculating the sensitivity score based on the one or more values andthe relative sensitivity rating for each of the one or more structuredobjects.

3. The computer-implemented data processing method of Concept 1, furthercomprising:

parsing the classification of one or more structured objects;

identifying each of the one or more structured objects having an emptyassociated value; and

modifying the classification of one or more structured objects to removethe identified one or more structured objects from the classification.

4. The computer-implemented data processing method of Concept 1, whereinrating the accuracy of the categorization comprises:

receiving a second electronic document that is related to the electronicdocument;

using one or more natural language processing techniques, by one or moreprocessors, to decompose data from the second electronic document into;

-   -   one or more second structured objects; and    -   one or more second values for each of the one or more structured        objects;

classifying, by one or more processors, each of the one or more secondstructured objects in the second electronic document based on one ormore second attributes of the one or more second structured objects;

categorizing, by one or more processors, each of the one or more secondstructured objects based on a sensitivity of the one or more secondstructured objects; and

comparing the categorization of the one or more structured objects withthe categorization of the one or more second structured objects; and

rating the accuracy based on the comparison.

5. The computer-implemented data processing method of Concept 1, whereinthe one or more natural language process techniques is selected from agroup comprising:

one or more optical character recognition techniques; and

one or more audio processing techniques.

6. The computer-implemented data processing method of Concept 1, whereinthe one or more attributes of the one or more structured objectscomprise a position within the electronic document of each of the one ormore structured objects in the electronic document.

7. The computer-implemented data processing method of Concept 1, whereinthe sensitivity of the one or more structured objects is automaticallydetermined based at least in part on one or more government regulationsdirected toward the type of information associated with the particularone or more structured objects.

8. The computer-implemented data processing of Concept 1, wherein ratingthe accuracy of the categorization of each of the one or more structuredobjects further comprises:

determining a character type for each of the one or more structuredobjects;

determining a character type for each value associated with each of theone or more structured objects;

comparing the character type for each value associated with each of theone or more structured objects and the character type for each of theone or more structed objects; and

rating the accuracy of the categorization of each of the one or morestructured objects based at least in part on comparing the charactertype for each value associated with each of the one or more structuredobjects and the character type for each of the one or more structedobjects.

9. A computer-implemented data processing method for automaticallyclassifying personal information in an electronic document andgenerating a sensitivity score for the electronic document based on theclassification, the method comprising:

receiving, by one or more processors, the electronic document foranalysis;

sorting, using one or more natural language processing techniques, datafrom the electronic document into;

-   -   one or more structured objects; and    -   one or more values for each of the one or more structured        objects;

classifying, by one or more processors, each of the one or morestructured objects in the electronic document based on one or moreattributes of the one or more structured objects;

categorizing, by one or more processors, each of the one or morestructured objects based on a sensitivity of the one or more structuredobjects;

generating, by one or more processors, a sensitivity score for theelectronic document based at least in part on the categorized one ormore structured objects and the associated one or more values;

parsing the classification of one or more structured objects;

identifying each of the one or more structured objects having an emptyassociated value; and

modifying the classification of one or more structured objects to removethe identified one or more structured objects from the classification.

10. The computer-implemented data processing method of Concept 9,wherein generating the sensitivity score for the electronic documentcomprises:

assigning a relative sensitivity rating to each of the one or morestructured objects; and

calculating the sensitivity score based on the one or more values andthe relative sensitivity rating for each of the one or more structuredobjects.

11. The computer-implemented data processing method of Concept 1,wherein rating the accuracy of the categorization comprises:

receiving a second electronic document that is related to the electronicdocument;

sorting, using one or more natural language processing techniques, thesecond electronic document into;

-   -   one or more second structured objects; and    -   one or more second values for each of the one or more structured        objects;

classifying, by one or more processors, each of the one or more secondstructured objects in the second electronic document based on one ormore second attributes of the one or more second structured objects;

categorizing, by one or more processors, each of the one or more secondstructured objects based on a sensitivity of the one or more secondstructured objects; and

generating, by one or more processors, a second sensitivity score forthe second electronic document based at least in part on the categorizedone or more second structured objects and the associated one or moresecond values;

parsing the classification of one or more second structured objects;

identifying each of the one or more second structured objects having anempty associated value; and

modifying the classification of one or more second structured objects toremove the identified one or more second structured objects from theclassification.

12. The computer-implemented data processing method of Concept 9,wherein the one or more natural language process techniques is selectedfrom a group comprising:

one or more optical character recognition techniques; and

one or more audio processing techniques.

13. The computer-implemented data processing method of Concept 9,wherein the one or more attributes of the one or more structured objectscomprise a position within the electronic document of each of the one ormore structured objects in the electronic document.

14. The computer-implemented data processing method of Concept 9,wherein the sensitivity of the one or more structured objects isautomatically determined based at least in part on one or moregovernment regulations directed toward the type of informationassociated with the particular one or more structured objects.

15. A computer-implemented data processing method for automaticallyclassifying personal information in an electronic document andgenerating a sensitivity score for the electronic document based on theclassification, the method comprising:

receiving, by one or more processors, the electronic document foranalysis;

using one or more natural language processing techniques, by one or moreprocessors, to decompose data from the electronic document into;

-   -   one or more structured objects; and    -   one or more values for each of the one or more structured        objects;

classifying, by one or more processors, each of the one or morestructured objects in the electronic document based on one or moreattributes of the one or more structured objects;

categorizing, by one or more processors, each of the one or morestructured objects based on a sensitivity of the one or more structuredobjects; and

generating, by one or more processors, a sensitivity score for theelectronic document based at least in part on the categorized one ormore structured objects and the associated one or more values.

16. The computer-implemented data processing method of Concept 15,wherein generating the sensitivity score for the electronic documentcomprises:

assigning a relative sensitivity rating to each of the one or morestructured objects; and

calculating the sensitivity score based on the one or more values andthe relative sensitivity rating for each of the one or more structuredobjects.

17. The computer-implemented data processing method of Concept 15,wherein rating the accuracy of the categorization comprises:

receiving a second electronic document that is related to the electronicdocument;

using one or more natural language processing techniques, by one or moreprocessors, to decompose data from the second electronic document into;

-   -   one or more second structured objects; and    -   one or more second values for each of the one or more structured        objects;

classifying, by one or more processors, each of the one or more secondstructured objects in the second electronic document based on one ormore second attributes of the one or more second structured objects;

categorizing, by one or more processors, each of the one or more secondstructured objects based on a sensitivity of the one or more secondstructured objects; and

comparing the categorization of the one or more structured objects withthe categorization of the one or more second structured objects; and

rating the accuracy based on the comparison.

18. The computer-implemented data processing method of Concept 15,wherein the one or more natural language process techniques is selectedfrom a group comprising:

one or more optical character recognition techniques; and

one or more audio processing techniques.

19. The computer-implemented data processing method of Concept 15,wherein the one or more attributes of the one or more structured objectscomprise a position within the electronic document of each of the one ormore structured objects in the electronic document.

20. The computer-implemented data processing method of Concept 1,wherein the sensitivity of the one or more structured objects isautomatically determined based at least in part on one or moregovernment regulations directed toward the type of informationassociated with the particular one or more structured objects.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments of a data subject access request fulfillment systemare described below. In the course of this description, reference willbe made to the accompanying drawings, which are not necessarily drawn toscale, and wherein:

FIG. 1 depicts a data subject request processing and fulfillment systemaccording to particular embodiments.

FIG. 2A is a schematic diagram of a computer (such as the data modelgeneration server 110, or data model population server 120 of FIG. 1)that is suitable for use in various embodiments of the data subjectrequest processing and fulfillment system shown in FIG. 1.

FIG. 2B is a flow chart depicting exemplary steps executed by a DataSubject Access

Request Routing Module according to a particular embodiment

FIGS. 3-43 are computer screen shots that demonstrate the operation ofvarious embodiments.

FIGS. 44-49 depict various exemplary screen displays and user interfacesthat a user of various embodiments of the system may encounter (FIGS. 47and 48 collectively show four different views of a Data Subject RequestQueue).

FIG. 50 is a flowchart showing an example of processes performed by anOrphaned Data Action Module 5000 according to various embodiments.

FIG. 51 is a flowchart showing an example of processes performed by aPersonal Data Deletion and Testing Module 5100 according to variousembodiments.

FIG. 52 is a flowchart showing an example of processes performed by aData Risk Remediation Module 5200 according to various embodiments.

FIG. 53 is a flowchart showing an example of processes performed by aCentral Consent Module 5300 according to various embodiments.

FIG. 54 is a flowchart showing an example of processes performed by aData Transfer Risk Identification Module 5400 according to variousembodiments.

FIG. 55 is a is a flowchart showing an example of a process performed byan Automated Classification Module 5500 according to particularembodiments.

FIG. 56 is a screenshot of a document from which the system describedherein may be configured to automatically classify personal information.

FIG. 57 depicts a visual representation of a plurality of objects thatthe system may create for each particular label identified in adocument.

FIGS. 58-60 depict a visual representation of the system creating aclassification and categorization of objects using contextualinformation from the document.

FIG. 61 depicts a visual representation of the system mapping valuesinto an object structure according to the classification andcategorization created as shown in FIGS. 57-59.

FIG. 62 depicts a visual representation of the mapped results of anautomatic classification of personal information in a document describedherein.

DETAILED DESCRIPTION

Various embodiments now will be described more fully hereinafter withreference to the accompanying drawings. It should be understood that theinvention may be embodied in many different forms and should not beconstrued as limited to the embodiments set forth herein. Rather, theseembodiments are provided so that this disclosure will be thorough andcomplete, and will fully convey the scope of the invention to thoseskilled in the art. Like numbers refer to like elements throughout.

Overview

Ticket management systems, according to various embodiments, are adaptedto receive data subject access requests (DSAR's) from particular datasubjects, and to facilitate the timely processing of valid DSAR's by anappropriate respondent. In particular embodiments, the ticket managementsystem receives DSAR's via one or more webforms that each may, forexample, respectively be accessed via an appropriate link/button on arespective web page. In other embodiments, the system may receive DSAR'sthrough any other suitable mechanism, such as via a computer softwareapplication (e.g., a messaging application such as Slack, Twitter), viaa chat bot, via generic API input from another system, or through entryby a representative who may receive the information, for example, viasuitable paper forms or over the phone.

The ticket management system may include a webform creation tool that isadapted to allow a user to create customized webforms for receivingDSAR's from various different data subject types and for routing therequests to appropriate individuals for processing. The webform creationtool may, for example, allow the user to specify the language that theform will be displayed in, what particular information is to berequested from the data subject and/or provided by the data subject, whoany DSAR's that are received via the webform will be routed to, etc. Inparticular embodiments, after the user completes their design of thewebform, the webform creation tool generates code for the webform thatmay be cut and then pasted into a particular web page.

The system may be further adapted to facilitate processing of DSAR'sthat are received via the webforms, or any other suitable mechanism. Forexample, the ticket management system may be adapted to execute one ormore of the following steps for each particular DSAR received via thewebforms (or other suitable mechanism) described above: (1) beforeprocessing the DSAR, confirm that the DSAR was actually submitted by theparticular data subject of the DSAR (or, for example, by an individualauthorized to make the DSAR on the data subject's behalf, such as aparent, guardian, power-of-attorney holder, etc.)—any suitable methodmay be used to confirm the identity of the entity/individual submittingthe DSAR—for example, if the system receives the DSAR via a third-partycomputer system, the system may validate authentication via API secret,or by requiring a copy of one or more particular legal documents (e.g.,a particular contract between two particular entities)—the system mayvalidate the identity of an individual by, for example, requiring theindividual (e.g., data subject) to provide particular accountcredentials, by requiring the individual to provide particularout-of-wallet information, through biometric scanning of the individual(e.g., finger or retinal scan), or via any other suitable identityverification technique; (2) if the DSAR was not submitted by theparticular data subject, deny the request; (3) if the DSAR was submittedby the particular data subject, advance the processing of the DSAR; (4)route the DSAR to the correct individual(s) or groups internally forhandling; (5) facilitate the assignment of the DSAR to one or more otherindividuals for handling of one or more portions of the DSAR; (6)facilitate the suspension of processing of the data subject's data bythe organization; and/or (7) change the policy according to which thedata subject's personal data is retained and/or processed by the system.In particular embodiments, the system may perform any one or more of theabove steps automatically. The system then generates a receipt for theDSAR request that the user can use as a transactional record of theirsubmitted request.

In particular embodiments, the ticket management system may be adaptedto generate a graphical user interface (e.g., a DSAR request-processingdashboard) that is adapted to allow a user (e.g., a privacy officer ofan organization that is receiving the DSAR) to monitor the progress ofany of the DSAR requests. The GUI interface may display, for each DSAR,for example, an indication of how much time is left (e.g., quantified indays and/or hours) before a legal and/or internal deadline to fulfillthe request. The system may also display, for each DSAR, a respectiveuser-selectable indicium that, when selected, may facilitate one or moreof the following: (1) verification of the request; (2) assignment of therequest to another individual; (3) requesting an extension to fulfillthe request; (4) rejection of the request; or (5) suspension of therequest.

As noted immediately above, and elsewhere in this application, inparticular embodiments, any one or more of the above steps may beexecuted by the system automatically. As a particular example, thesystem may be adapted to automatically verify the identity of the DSARrequestor and then automatically fulfill the DSAR request by, forexample, obtaining the requested information via a suitable data modeland communicating the information to the requestor. As anotherparticular example, the system may be configured to automatically routethe DSAR to the correct individual for handling based at least in parton one or more pieces of information provided (e.g., in the webform).

In various embodiments, the system may be adapted to prioritize theprocessing of DSAR's based on metadata about the data subject of theDSAR. For example, the system may be adapted for: (1) in response toreceiving a DSAR, obtaining metadata regarding the data subject; (2)using the metadata to determine whether a priority of the DSAR should beadjusted based on the obtained metadata; and (3) in response todetermining that the priority of the DSAR should be adjusted based onthe obtained metadata, adjusting the priority of the DSAR.

Examples of metadata that may be used to determine whether to adjust thepriority of a particular DSAR include: (1) the type of request; (2) thelocation from which the request is being made; (3) the country ofresidency of the data subject and, for example, that county's tolerancefor enforcing DSAR violations; (4) current sensitivities to worldevents; (5) a status of the requestor (e.g., especially loyal customer);or (6) any other suitable metadata.

In particular embodiments, any entity (e.g., organization, company,etc.) that collects, stores, processes, etc. personal data may requireone or more of: (1) consent from a data subject from whom the personaldata is collected and/or processed; and/or (2) a lawful basis for thecollection and/or processing of the personal data. In variousembodiments, the entity may be required to, for example, demonstratethat a data subject has freely given specific, informed, and unambiguousindication of the data subject's agreement to the processing of his orher personal data for one or more specific purposes (e.g., in the formof a statement or clear affirmative action). As such, in particularembodiments, an organization may be required to demonstrate a lawfulbasis for each piece of personal data that the organization hascollected, processed, and/or stored. In particular, each piece ofpersonal data that an organization or entity has a lawful basis tocollect and process may be tied to a particular processing activityundertaken by the organization or entity.

A particular organization may undertake a plurality of different privacycampaigns, processing activities, etc. that involve the collection andstorage of personal data. In some embodiments, each of the plurality ofdifferent processing activities may collect redundant data (e.g., maycollect the same personal data for a particular individual more thanonce), and may store data and/or redundant data in one or moreparticular locations (e.g., on one or more different servers, in one ormore different databases, etc.). In this way, because of the number ofprocessing activities that an organization may undertake, and the amountof data collected as part of those processing activities over time, oneor more data systems associated with an entity or organization may storeor continue to store data that is not associated with any particularprocessing activity (e.g., any particular current processing activity).Under various legal and industry standards related to the collection andstorage of personal data, the organization or entity may not have or mayno longer have a legal basis to continue to store the data. As such,organizations and entities may require improved systems and methods toidentify such orphaned data, and take corrective action, if necessary(e.g., to ensure that the organization may not be in violation of one ormore legal or industry regulations).

In various embodiments, an orphaned personal data identification systemmay be configured to generate a data model (e.g., one or more datamodels) that maps one or more relationships between and/or among aplurality of data assets utilized by a corporation or other entity(e.g., individual, organization, etc.) in the context, for example, ofone or more business processes or processing activities. In particularembodiments, the system is configured to generate and populate a datamodel substantially on the fly (e.g., as the system receives new dataassociated with particular processing activities). In still otherembodiments, the system is configured to generate and populate a datamodel based at least in part on existing information stored by thesystem (e.g., in one or more data assets), for example, using one ormore suitable scanning techniques. In still other embodiments, thesystem is configured to access an existing data model that maps personaldata stored by one or more organization systems to particular associatedprocessing activities.

In various embodiments, the system may analyze the data model toidentify personal data that has been collected and stored using one ormore computer systems operated and/or utilized by a particularorganization where the personal data is not currently being used as partof any privacy campaigns, processing activities, etc. undertaken by theparticular organization. This data may be described as orphaned data. Insome circumstances, the particular organization may be exposed to anincreased risk that the data may be accessed by a third party (e.g.,cybercrime) or that the particular organization may not be in compliancewith one or more legal or industry requirements related to thecollection, storage, and/or processing of this orphaned data.

Additionally, in some implementations, in response to the termination ofa particular privacy campaign, processing activity, (e.g., manually orautomatically), the system may be configured to analyze the data modelto determine whether any of the personal data that has been collectedand stored by the particular organization is now orphaned data (e.g.,whether any personal data collected and stored as part of thenow-terminated privacy campaign is being utilized by any otherprocessing activity, has some other legal basis for its continuedstorage, etc.).

In additional implementations in response to determining that aparticular privacy campaign, processing activity, etc. has not beenutilized for a period of time (e.g., a day, month, year), the system maybe configured to terminate the particular privacy campaign, processingactivity, etc. or prompt one or more individuals associated with theparticular organization to indicate whether the particular privacycampaign, processing activity, etc. should be terminated or otherwisediscontinued.

For example, a particular processing activity may include transmissionof a periodic advertising e-mail for a particular company (e.g., ahardware store). As part of the processing activity, the particularcompany may have collected and stored e-mail addresses for customersthat elected to receive (e.g., consented to the receipt of) promotionale-mails. In response to determining that the particular company has notsent out any promotional e-mails for at least a particular amount oftime (e.g., for at least a particular number of months), the system maybe configured to: (1) automatically terminate the processing activity;(2) identify any of the personal data collected as part of theprocessing activity that is now orphaned data (e.g., the e-mailaddresses); and (3) automatically delete the identified orphaned data.The processing activity may have ended for any suitable reason (e.g.,because the promotion that drove the periodic e-mails has ended). As maybe understood in light of this disclosure, because the particularorganization no longer has a valid basis for continuing to store thee-mail addresses of the customers once the e-mail addresses are nolonger being used to send promotional e-mails, the organization may wishto substantially automate the removal of personal data stored in itscomputer systems that may place the organization in violation of one ormore personal data storage rules or regulations.

When the particular privacy campaign, processing activity, etc. isterminated or otherwise discontinued, the system may use the data modelto determine if any of the associated personal data that has beencollected and stored by the particular organization is now orphaneddata.

In various embodiments, the system may be configured to identifyorphaned data of a particular organization and automatically delete thedata. In some implementations, in response to identifying the orphaneddata, the system may present the data to one or more individualsassociated with the particular organization (e.g., a privacy officer)and prompt the one or more individuals to indicate why the orphaned datais being stored by the particular organization. The system may thenenable the individual to provide one or more valid reasons for thedata's continued storage, or enable the one or more individuals todelete the particular orphaned data. In some embodiments, the system mayautomatically delete the orphaned data if, for example: (1) in responseto determining that a reason provided by the individual is not asufficient basis for the continued storage of the personal data; (2) theindividual does not respond to the request to provide one or more validreasons in a timely manner; (3) etc. In some embodiments, one or moreother individuals may review the response provided indicating why theorphaned data is being stored, and in some embodiments, the one or moreother individuals can delete the particular orphaned data.

In various embodiments, the system may be configured to review the datacollection policy (e.g., how data is acquired, security of data storage,who can access the data, etc.) for the particular organization as wellas one or more data retention metrics for the organization. For example,the one or more data retention metrics may include how much personaldata is being collected, how long the data is held, how many privacycampaigns or other processes are using the personal data, etc.Additionally, the system may compare the particular organization's datacollection policy and data retention metrics to the industry standards(e.g., in a particular field, based on a company size, etc.). In variousembodiments, the system may be configured to generate a report thatincludes the comparison and provide the report to the particularorganization (e.g., in electronic format).

In particular embodiments, the system may be configured advise theparticular organization to delete data and identify particular data thatshould be deleted. In some embodiments, the system may automaticallydelete particular data (e.g., orphaned data). Further, the system may beconfigured to calculate and provide a risk score for particular data orthe organization's data collection policy overall. In particularembodiments, the system may be configured to calculate the risk scorebased on the combinations of personal data elements in the datainventory of the organization (e.g., where an individual's phone numberis stored in one location and their mailing address is stored in anotherlocation), and as such the risk may be increased because the additionalpieces of personal information can make the stored data more sensitive.

In particular embodiments, any entity (e.g., organization, company,etc.) that collects, stores, processes, etc. personal data may requireone or more of: (1) consent from a data subject from whom the personaldata is collected and/or processed; and/or (2) a lawful basis for thecollection and/or processing of the personal data. In variousembodiments, the entity may be required to, for example, demonstratethat a data subject has freely given specific, informed, and unambiguousindication of the data subject's agreement to the processing of his orher personal data for one or more specific purposes (e.g., in the formof a statement or clear affirmative action). As such, in particularembodiments, an organization may be required to demonstrate a lawfulbasis for each piece of personal data that the organization hascollected, processed, and/or stored. In particular, each piece ofpersonal data that an organization or entity has a lawful basis tocollect and process may be tied to a particular processing activityundertaken by the organization or entity.

A particular organization may undertake a plurality of different privacycampaigns, processing activities, etc. that involve the collection andstorage of personal data. In some embodiments, each of the plurality ofdifferent processing activities may collect redundant data (e.g., maycollect the same personal data for a particular individual more thanonce), and may store data and/or redundant data in one or moreparticular locations (e.g., on one or more different servers, in one ormore different databases, etc.). In this way, because of the number ofprocessing activities that an organization may undertake, and the amountof data collected as part of those processing activities over time, oneor more data systems associated with an entity or organization may storeor continue to store data that is not associated with any particularprocessing activity (e.g., any particular current processing activity).Under various legal and industry standards related to the collection andstorage of personal data, such data may not have or may no longer have alegal basis for the organization or entity to continue to store thedata. As such, organizations and entities may require improved systemsand methods to maintain an inventory of data assets utilized to processand/or store personal data for which a data subject has provided consentfor such storage and/or processing.

In various embodiments, the system is configured to provide athird-party data repository system to facilitate the receipt andcentralized storage of personal data for each of a plurality ofrespective data subjects, as described herein. Additionally, thethird-party data repository system is configured to interface with acentralized consent receipt management system.

In particular embodiments, the system may be configured to use one ormore website scanning tools to, for example, identify a form (e.g., awebform) and locate a data asset where the input data is transmitted(e.g., Salesforce). Additionally, the system may be configured to addthe data asset to the third-party data repository (e.g., and/or datamap/data inventory) with a link to the form. In response to a userinputting form data (e.g., name, address, credit card information, etc.)of the form and submitting the form, the system may, based on the linkto the form, create a unique subject identifier to submit to thethird-party data repository and, along with the form data, to the dataasset. Further, the system may use the unique subject identifier of auser to access and update each of the data assets of the particularorganization. For example, in response to a user submitting a datasubject access request to delete the user's personal data that theparticular organization has stored, the system may use the uniquesubject identifier of the user to access and delete the user's personaldata stored in all of the data assets (e.g., Salesforce, Eloqua,Marketo, etc.) utilized by the particular organization.

The system may, for example: (1) generate, for each of a plurality ofdata subjects, a respective unique subject identifier in response tosubmission, by each data subject, of a particular form; (2) maintain adatabase of each respective unique subject identifier; and (3)electronically link each respective unique subject identifier to eachof: (A) a form initially submitted by the user; and (B) one or more dataassets that utilize data received from the data subject via the form.

In various embodiments, the system may be configured to, for example:(1) identify a form used to collect one or more pieces of personal data,(2) determine a data asset of a plurality of data assets of theorganization where input data of the form is transmitted, (3) add thedata asset to the third-party data repository with an electronic link tothe form, (4) in response to a user submitting the form, create a uniquesubject identifier to submit to the third-party data repository and,along with the form data provided by the user in the form, to the dataasset, (5) submit the unique subject identifier and the form dataprovided by the user in the form to the third-party data repository andthe data asset, and (6) digitally store the unique subject identifierand the form data provided by the user in the form in the third-partydata repository and the data asset.

In some embodiments, the system may be further configured to, forexample: (1) receive a data subject access request from the user (e.g.,a data subject rights' request, a data subject deletion request, etc.),(2) access the third-party data repository to identify the uniquesubject identifier of the user, (3) determine which data assets of theplurality of data assets of the organization include the unique subjectidentifier, (4) access personal data of the user stored in each of thedata assets of the plurality of data assets of the organization thatinclude the unique subject identifier, and (5) take one or more actionsbased on the data subject access request (e.g., delete the accessedpersonal data in response to a data subject deletion request).

Various privacy and security policies (e.g., such as the EuropeanUnion's General Data Protection Regulation, and other such policies) mayprovide data subjects (e.g., individuals, organizations, or otherentities) with certain rights related to the data subject's personaldata that is collected, stored, or otherwise processed by an entity. Inparticular, under various privacy and security policies, a data subjectmay be entitled to a right to erasure of any personal data associatedwith that data subject that has been at least temporarily stored by theentity (e.g., a right to be forgotten). In various embodiments, underthe right to erasure, an entity (e.g., a data controller on behalf ofanother organization) may be obligated to erase personal data withoutundue delay under one or more of the following conditions: (1) thepersonal data is no longer necessary in relation to a purpose for whichthe data was originally collected or otherwise processed; (2) the datasubject has withdrawn consent on which the processing of the personaldata is based (e.g., and there is no other legal grounds for suchprocessing); (3) the personal data has been unlawfully processed; (4)the data subject has objected to the processing and there is nooverriding legitimate grounds for the processing of the data by theentity; and/or (5) for any other suitable reason or under any othersuitable conditions.

In particular embodiments, a personal data deletion system may beconfigured to: (1) at least partially automatically identify and deletepersonal data that an entity is required to erase under one or more ofthe conditions discussed above; and (2) perform one or more data testsafter the deletion to confirm that the system has, in fact, deleted anypersonal data associated with the data subject.

In particular embodiments, in response to a data subject submitting arequest to delete their personal data from an entity's systems, thesystem may, for example: (1) automatically determine where the datasubject's personal data is stored; and (2) in response to determiningthe location of the data (which may be on multiple computing systems),automatically facilitate the deletion of the data subject's personaldata from the various systems (e.g., by automatically assigning aplurality of tasks to delete data across multiple business systems toeffectively delete the data subject's personal data from the systems).In particular embodiments, the step of facilitating the deletion maycomprise, for example: (1) overwriting the data in memory; (2) markingthe data for overwrite; (2) marking the data as free (e.g., deleting adirectory entry associated with the data); and/or (3) using any othersuitable technique for deleting the personal data. In particularembodiments, as part of this process, the system may use any suitabledata modelling technique to efficiently determine where all of the datasubject's personal data is stored.

In various embodiments, the system may be configured to store (e.g., inmemory) an indication that the data subject has requested to delete anyof their personal data stored by the entity has been processed. Undervarious legal and industry policies/standards, the entity may have acertain period of time (e.g., a number of days) in order to comply withthe one or more requirements related to the deletion or removal ofpersonal data in response to receiving a request from the data subjector in response to identifying one or more of the conditions requiringdeletion discussed above. In response to the receiving of an indicationthat the deletion request for the data subject's personal data has beenprocessed or the certain period of time (described above) has passed,the system may be configured to perform a data test to confirm thedeletion of the data subject's personal data.

In particular embodiments, when performing the data test, the system maybe configured to provide an interaction request to the entity on behalfof the data subject. In particular embodiments, the interaction requestmay include, for example, a request for one or more pieces of dataassociated with the data subject (e.g., account information, etc.). Invarious embodiments, the interaction request is a request to contact thedata subject (e.g., for any suitable reason). The system may, forexample, be configured to substantially automatically complete acontact-request form (e.g., a webform made available by the entity) onbehalf of the data subject. In various embodiments, when automaticallycompleting the form on behalf of the data subject, the system may beconfigured to only provide identifying data, but not provide any contactdata. In response to submitting the interaction request (e.g.,submitting the webform), the system may be configured to determinewhether the one or more computers systems have generated and/ortransmitted a response to the data subject. The system may be configuredto determine whether the one or more computers systems have generatedand/or transmitted the response to the data subject by, for example,analyzing one or more computer systems associated with the entity todetermine whether the one or more computer systems have generated acommunication to the data subject (e.g., automatically) for transmissionto an e-mail address or other contact method associated with the datasubject, generated an action-item for an individual to contact the datasubject at a particular contact number, etc.

In response to determining that the one or more computer systems hasgenerated and/or transmitted the response to the data subject, thesystem may be configured to determine that the one or more computersystems has not complied with the data subject's request for deletion oftheir personal data from the one or more computers systems associatedwith the entity. In response, the system may generate an indication thatthe one or more computer systems has not complied with the datasubject's request for deletion of their personal data from the one ormore computers systems have, and store the indication in computermemory.

To perform the data test, for example, the system may be configured to:(1) access (e.g., manually or automatically) a form for the entity(e.g., a web-based “Contact Us” form); (2) input a unique identifierassociated with the data subject (e.g., a full name or customer IDnumber) without providing contact information for the data subject(e.g., mailing address, phone number, email address, etc.); and (3)input a request, within the form, for the entity to contact the datasubject to provide information associated with the data subject (e.g.,the data subject's account balance with the entity). In response tosubmitting the form to the entity, the system may be configured todetermine whether the data subject is contacted (e.g., via a phone callor email) by the one or more computer systems (e.g., automatically). Inresponse to determining that the data subject has been contactedfollowing submission of the form, the system may determine that the oneor more computer systems have not fully deleted the data subject'spersonal data (e.g., because the one or more computer systems must stillbe storing contact information for the data subject in at least onelocation).

In particular embodiments, the system is configured to generate one ormore test profiles for one or more test data subjects. For each of theone or more test data subjects, the system may be configured to generateand store test profile data such as, for example: (1) name; (2) address;(3) telephone number; (4) e-mail address; (5) social security number;(6) information associated with one or more credit accounts (e.g.,credit card numbers); (7) banking information; (8) location data; (9)internet search history; (10) non-credit account data; and/or (11) anyother suitable test data. The system may then be configured to at leastinitially consent to processing or collection of personal data for theone or more test data subjects by the entity. The system may thenrequest deletion, by the entity, of any personal data associated with aparticular test data subject. In response to requesting the deletion ofdata for the particular test data subject, the system may then take oneor more actions using the test profile data associated with theparticular test data subjects in order to confirm that the one or morecomputers systems have, in fact, deleted the test data subject'spersonal data (e.g., any suitable action described herein). The systemmay, for example, be configured to: (1) initiate a contact request onbehalf of the test data subject; (2) attempt to login to one or moreuser accounts that the system had created for the particular test datasubject; and/or (3) take any other action, the effect of which couldindicate a lack of complete deletion of the test data subject's personaldata.

In response to determining that the one or more computer systems havenot fully deleted a data subject's (or test data subject's) personaldata, the system may then be configured, in particular embodiments, to:(1) flag the data subject's personal data for follow up by one or moreprivacy officers to investigate the lack of deletion; (2) perform one ormore scans of one or more computing systems associated with the entityto identify any residual personal data that may be associated with thedata subject; (3) generate a report indicating the lack of completedeletion; and/or (4) take any other suitable action to flag forfollow-up the data subject, personal data, initial request to beforgotten, etc.

The system may, for example, be configured to test to ensure the datahas been deleted by: (1) submitting a unique token of data through aform to a system (e.g., mark to); (2) in response to passage of anexpected data retention time, test the system by calling into the systemafter the passage of the data retention time to search for the uniquetoken. In response to finding the unique token, the system may beconfigured to determine that the data has not been properly deleted.

In various embodiments, a system may be configured to substantiallyautomatically determine whether to take one or more actions in responseto one or more identified risk triggers. For example, an identified risktrigger may be that a data asset for an organization is hosted in onlyone particular location thereby increasing the scope of risk if thelocation were infiltrated (e.g., via cybercrime). In particularembodiments, the system is configured to substantially automaticallyperform one or more steps related to the analysis of and response to theone or more potential risk triggers discussed above. For example, thesystem may substantially automatically determine a relevance of a riskposed by (e.g., a risk level) the one or more potential risk triggersbased at least in part on one or more previously-determined responses tosimilar risk triggers. This may include, for example, one or morepreviously determined responses for the particular entity that hasidentified the current risk trigger, one or more similarly situatedentities, or any other suitable entity or potential trigger.

In particular embodiments, the system may, for example, be configuredto: (1) receive risk remediation data for a plurality of identified risktriggers from a plurality of different entities; (2) analyze the riskremediation data to determine a pattern in assigned risk levels anddetermined response to particular risk triggers; and (3) develop a modelbased on the risk remediation data for use in facilitating an automaticassessment of and/or response to future identified risk triggers.

In some embodiments, when a change or update is made to one or moreprocessing activities and/or data assets (e.g., a database associatedwith a particular organization), the system may use data modelingtechniques to update the risk remediation data for use in facilitatingan automatic assessment of and/or response to future identified risktriggers. In various embodiments, when a privacy campaign, processingactivity, etc. of the particular organization is modified (e.g., add,remove, or update particular information), then the system may use therisk remediation data for use in facilitating an automatic assessment ofand/or response to future identified risk triggers.

In particular embodiments, the system may, for example, be configuredto: (1) access risk remediation data for an entity that identifies oneor more suitable actions to remediate a risk in response to identifyingone or more data assets of the entity that may be affected by one ormore potential risk triggers; (2) receive an indication of an update tothe one or more data assets; (3) identify one or more potential updatedrisk triggers for an entity; (4) assess and analyze the one or morepotential updated risk triggers to determine a relevance of a risk posedto the entity by the one or more potential updated risk triggers; (5)use one or more data modeling techniques to identify one or more dataassets associated with the entity that may be affected by the risk; and(6) update the risk remediation data to include the one or more actionsto remediate the risk in response to identifying the one or morepotential updated risk triggers.

In any embodiment described herein, an automated classification systemmay be configured to substantially automatically classify one or morepieces of personal information in one or more documents (e.g., one ormore text-based documents, one or more spreadsheets, one or more PDFs,one or more webpages, etc.). In particular embodiments, the system maybe implemented in the context of any suitable privacy compliance system,which may, for example, be configured to calculate and assign asensitivity score to a particular document based at least in part on oneor more determined categories of personal information (e.g., personaldata) identified in the one or more documents. As understood in the art,the storage of particular types of personal information may be governedby one or more government or industry regulations. As such, it may bedesirable to implement one or more automated measures to automaticallyclassify personal information from stored documents (e.g., to determinewhether such documents may require particular security measures, storagetechniques, handling, whether the documents should be destroyed, etc.).

Exemplary Technical Platforms

As will be appreciated by one skilled in the relevant field, the presentinvention may be, for example, embodied as a computer system, a method,or a computer program product. Accordingly, various embodiments may takethe form of an entirely hardware embodiment, an entirely softwareembodiment, or an embodiment combining software and hardware aspects.Furthermore, particular embodiments may take the form of a computerprogram product stored on a computer-readable storage medium havingcomputer-readable instructions (e.g., software) embodied in the storagemedium. Various embodiments may take the form of web-implementedcomputer software. Any suitable computer-readable storage medium may beutilized including, for example, hard disks, compact disks, DVDs,optical storage devices, and/or magnetic storage devices.

Various embodiments are described below with reference to block diagramsand flowchart illustrations of methods, apparatuses (e.g., systems), andcomputer program products. It should be understood that each block ofthe block diagrams and flowchart illustrations, and combinations ofblocks in the block diagrams and flowchart illustrations, respectively,can be implemented by a computer executing computer programinstructions. These computer program instructions may be loaded onto ageneral-purpose computer, special-purpose computer, or otherprogrammable data processing apparatus to produce a machine, such thatthe instructions which execute on the computer or other programmabledata processing apparatus to create means for implementing the functionsspecified in the flowchart block or blocks.

These computer program instructions may also be stored in acomputer-readable memory that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner such that the instructions stored in the computer-readable memoryproduce an article of manufacture that is configured for implementingthe function specified in the flowchart block or blocks. The computerprogram instructions may also be loaded onto a computer or otherprogrammable data processing apparatus to cause a series of operationalsteps to be performed on the computer or other programmable apparatus toproduce a computer implemented process such that the instructions thatexecute on the computer or other programmable apparatus provide stepsfor implementing the functions specified in the flowchart block orblocks.

Accordingly, blocks of the block diagrams and flowchart illustrationssupport combinations of mechanisms for performing the specifiedfunctions, combinations of steps for performing the specified functions,and program instructions for performing the specified functions. Itshould also be understood that each block of the block diagrams andflowchart illustrations, and combinations of blocks in the blockdiagrams and flowchart illustrations, can be implemented by specialpurpose hardware-based computer systems that perform the specifiedfunctions or steps, or combinations of special purpose hardware andother hardware executing appropriate computer instructions.

Example System Architecture

FIG. 1 is a block diagram of a data subject access request processingand fulfillment system 100 according to a particular embodiment. Invarious embodiments, the data subject access request processing andfulfillment system is part of a privacy compliance system (also referredto as a privacy management system), or other system, which may, forexample, be associated with a particular organization and be configuredto aid in compliance with one or more legal or industry regulationsrelated to the collection and storage of personal data.

As may be understood from FIG. 1, the data subject access requestprocessing and fulfillment system 100 includes one or more computernetworks 115, a Data Model Generation Server 110, a Data ModelPopulation Server 120, an Intelligent Identity Scanning Server 130(which may automatically validate a DSAR requestor's identity). One orMore Databases 140 or other data structures, one or more remotecomputing devices 150 (e.g., a desktop computer, laptop computer, tabletcomputer, smartphone, etc.), and One or More Third Party Servers 160. Inparticular embodiments, the one or more computer networks 115 facilitatecommunication between the Data Model Generation Server 110, Data ModelPopulation Server 120, Intelligent Identity Scanning/Verification Server130, One or More Databases 140, one or more remote computing devices 150(e.g., a desktop computer, laptop computer, tablet computer, smartphone,etc.), One or More Third Party Servers 160, and DSAR Processing andFulfillment Server 170. Although in the embodiment shown in FIG. 1, theData Model Generation Server 110, Data Model Population Server 120,Intelligent Identity Scanning Server 130, One or More Databases 140, oneor more remote computing devices 150 (e.g., a desktop computer, laptopcomputer, tablet computer, smartphone, etc.), and One or More ThirdParty Servers 160, and DSAR Processing and Fulfillment Server 170 areshown as separate servers, it should be understood that in otherembodiments, the functionality of one or more of these servers and/orcomputing devices may, in different embodiments, be executed by a largeror smaller number of local servers, one or more cloud-based servers, orany other suitable configuration of computers.

The one or more computer networks 115 may include any of a variety oftypes of wired or wireless computer networks such as the Internet, aprivate intranet, a public switch telephone network (PSTN), or any othertype of network. The communication link between the DSAR Processing andFulfillment Server 170 and the One or More Remote Computing Devices 150may be, for example, implemented via a Local Area Network (LAN) or viathe Internet. In other embodiments, the One or More Databases 140 may bestored either fully or partially on any suitable server or combinationof servers described herein.

FIG. 2A illustrates a diagrammatic representation of a computer 200 thatcan be used within the data subject access request processing andfulfillment system 100, for example, as a client computer (e.g., one ormore remote computing devices 150 shown in FIG. 1), or as a servercomputer (e.g., Data Model Generation Server 110 shown in FIG. 1). Inparticular embodiments, the computer 200 may be suitable for use as acomputer within the context of the data subject access requestprocessing and fulfillment system 100 that is configured for routingand/or processing DSAR requests and/or generating one or more datamodels used in automatically fulfilling those requests.

In particular embodiments, the computer 200 may be connected (e.g.,networked) to other computers in a LAN, an intranet, an extranet, and/orthe Internet. As noted above, the computer 200 may operate in thecapacity of a server or a client computer in a client-server networkenvironment, or as a peer computer in a peer-to-peer (or distributed)network environment. The Computer 200 may be a personal computer (PC), atablet PC, a set-top box (STB), a Personal Digital Assistant (PDA), acellular telephone, a web appliance, a server, a network router, aswitch or bridge, or any other computer capable of executing a set ofinstructions (sequential or otherwise) that specify actions to be takenby that computer. Further, while only a single computer is illustrated,the term “computer” shall also be taken to include any collection ofcomputers that individually or jointly execute a set (or multiple sets)of instructions to perform any one or more of the methodologiesdiscussed herein.

An exemplary computer 200 includes a processing device 202, a mainmemory 204 (e.g., read-only memory (ROM), flash memory, dynamic randomaccess memory (DRAM) such as synchronous DRAM (SDRAM) or Rambus DRAM(RDRAM), etc.), static memory 206 (e.g., flash memory, static randomaccess memory (SRAM), etc.), and a data storage device 218, whichcommunicate with each other via a bus 232.

The processing device 202 represents one or more general-purposeprocessing devices such as a microprocessor, a central processing unit,or the like. More particularly, the processing device 202 may be acomplex instruction set computing (CISC) microprocessor, reducedinstruction set computing (RISC) microprocessor, very long instructionword (VLIW) microprocessor, or processor implementing other instructionsets, or processors implementing a combination of instruction sets. Theprocessing device 202 may also be one or more special-purpose processingdevices such as an application specific integrated circuit (ASIC), afield programmable gate array (FPGA), a digital signal processor (DSP),network processor, or the like. The processing device 202 may beconfigured to execute processing logic 226 for performing variousoperations and steps discussed herein.

The computer 120 may further include a network interface device 208. Thecomputer 200 also may include a video display unit 210 (e.g., a liquidcrystal display (LCD) or a cathode ray tube (CRT)), an alphanumericinput device 212 (e.g., a keyboard), a cursor control device 214 (e.g.,a mouse), and a signal generation device 216 (e.g., a speaker).

The data storage device 218 may include a non-transitorycomputer-accessible storage medium 230 (also known as a non-transitorycomputer-readable storage medium or a non-transitory computer-readablemedium) on which is stored one or more sets of instructions (e.g.,software instructions 222) embodying any one or more of themethodologies or functions described herein. The software instructions222 may also reside, completely or at least partially, within mainmemory 204 and/or within processing device 202 during execution thereofby computer 200—main memory 204 and processing device 202 alsoconstituting computer-accessible storage media. The softwareinstructions 222 may further be transmitted or received over a network115 via network interface device 208.

While the computer-accessible storage medium 230 is shown in anexemplary embodiment to be a single medium, the term“computer-accessible storage medium” should be understood to include asingle medium or multiple media (e.g., a centralized or distributeddatabase, and/or associated caches and servers) that store the one ormore sets of instructions. The terms “computer-accessible storagemedium”, “computer-readable medium”, and like terms should also beunderstood to include any medium that is capable of storing, encoding orcarrying a set of instructions for execution by the computer and thatcause the computer to perform any one or more of the methodologies ofthe present invention. These terms should accordingly be understood toinclude, but not be limited to, solid-state memories, optical andmagnetic media, etc.

Systems for Managing Data Subject Access Requests

In various embodiments, the system may include a ticket managementsystem and/or other systems for managing data subject access requests.In operation, the system may use one or more computer processors, whichare operatively coupled to memory, to execute one or more softwaremodules (which may be included in the Instructions 222 referenced above)such as: (1) a DSAR Request Routing Module 1000; and (4) a DSARPrioritization Module. An overview of the functionality and operation ofeach of these modules is provided below.

Data Subject Access Request Routing Module 1000

As shown in FIG. 2B, a Data Subject Access Request Routing Module 1000,according to particular embodiments, is adapted for executing the stepsof: (1) at Step 1050, presenting, by at least one computer processor, afirst webform on a first web site, the first webform being adapted toreceive data subject access requests and to route the requests to afirst designated individual (e.g., an individual who is associated witha first sub-organization of a particular organization—e.g., an employeeof the first sub-organization) for processing (in various embodiments,“presenting a webform on a website” may comprise, for example: (A)providing a button, link, or other selectable indicium on the websitethat, when selected, causes the system to display the webform, or (B)displaying the webform directly on the website); (2) at Step 1100presenting, by at least one computer processor, a second webform on asecond website, the second webform being adapted to receive data subjectaccess requests and to route the requests to a second designatedindividual (e.g., an individual who is associated with a secondsub-organization of a particular organization—e.g., an employee of thesecond sub-organization) for processing; (3) at Step 1150, receiving, byat least one computer processor, via the first webform, a first datasubject access request; (4) at Step 1200, at least partially in responseto the receiving the first data subject access request, automaticallyrouting the first data subject access request to the first designatedindividual for handling; (5) at Step 1250, at least partially inresponse to the receiving the second data subject access request,automatically routing the second data subject access request to thesecond designated individual for handling; and (6) at Step 1300,communicating, via a single user interface, a status of both the firstdata subject access request and the second data subject access request.

In particular embodiments: (1) the first website is a website of a firstsub-organization of a particular parent organization; (2) the secondwebsite is a website of a second sub-organization of the particularparent organization; and (3) the computer-implemented method furthercomprises communicating, by at least one computer processor, via asingle user interface, a status of each of said first data subjectaccess request and said second data subject access request (e.g., to anemployee of—e.g., privacy officer of—the parent organization). Asdiscussed in more detail below, this single user interface may displayan indication, for each respective one of the first and second datasubject access requests, of a number of days remaining until a deadlinefor fulfilling the respective data subject access request.

In certain embodiments, the single user interface is adapted tofacilitate the deletion or assignment of multiple data subject accessrequests to a particular individual for handling in response to a singlecommand from a user (e.g., in response to a user first selectingmultiple data subject access requests from the single user interface andthen executing an assign command to assign each of the multiple requeststo a particular individual for handling).

In particular embodiments, the system running the Data Subject AccessRequest Routing Module 1000, according to particular embodiments, may beadapted for, in response to receiving each data subject access request,generating an ID number (e.g., a transaction ID or suitableAuthentication Token) for the first data subject access request, whichmay be used later, by the DSAR requestor, to access information relatedto the DSAR, such as personal information requested via the DSAR, thestatus of the DSAR request, etc. To facilitate this, the system may beadapted for receiving the ID number from an individual and, at leastpartially in response to receiving the ID number from the individual,providing the individual with information regarding status of the datasubject access request and/or information previously requested via thedata subject access request.

In particular embodiments, the system may be adapted to facilitate theprocessing of multiple different types of data subject access requests.For example, the system may be adapted to facilitate processing: (1)requests for all personal data that an organization is processing forthe data subject (a copy of the personal data in a commonly used,machine-readable format); (2) requests for all such personal data to bedeleted; (3) requests to update personal data that the organization isstoring for the data subject; (4) requests to opt out of having theorganization use the individual's personal information in one or moreparticular ways (e.g., per the organization's standard businesspractices), or otherwise change the way that the organization uses theindividual's personal information; and/or (5) the filing of complaints.

In particular embodiments, the system may execute one or more steps(e.g., any suitable step or steps discussed herein) automatically. Forexample, the system may be adapted for: (1) receiving, from the firstdesignated individual, a request to extend a deadline for satisfying thefirst data subject access request; (2) at least partially in response toreceiving the extension request, automatically determining, by at leastone processor, whether the requested extension complies with one or moreapplicable laws or internal policies; and (3) at least partially inresponse to determining that the requested extension complies with theone or more applicable laws or internal policies, automaticallymodifying the deadline, in memory, to extend the deadline according tothe extension request. The system may be further adapted for, at leastpartially in response to determining that the requested extension doesnot comply with the one or more applicable laws or internal policies,automatically rejecting the extension request. In various embodiments,the system may also, or alternatively, be adapted for: (1) at leastpartially in response to determining that the requested extension doesnot comply with the one or more applicable laws or internal policies,automatically modifying the length of the requested extension to complywith the one or more applicable laws or internal policies; and (2)automatically modifying the deadline, in memory, to extend the deadlineaccording to the extension request.

In various embodiments, the system may be adapted for: (1) automaticallyverifying an identity of a particular data subject access requestorplacing the first data subject access request; (2) at least partially inresponse to verifying the identity of the particular data subject accessrequestor, automatically obtaining, from a particular data model, atleast a portion of information requested in the first data subjectaccess request; and (3) after obtaining the at least a portion of therequested information, displaying the obtained information to a user aspart of a fulfillment of the first data subject access request. Theinformation requested in the first data subject access request may, forexample, comprise at least substantially all (e.g., most or all) of theinformation regarding the first data subject that is stored within thedata model.

In various embodiments, the system is adapted for: (1) automaticallyverifying, by at least one computer processor, an identity of aparticular data subject access requestor placing the first data subjectaccess request; and (2) at least partially in response to verifying theidentity of the particular data subject access requestor, automaticallyfacilitating an update of personal data that an organization associatedwith the first webform is processing regarding the particular datasubject access requestor.

Similarly, in particular embodiments, the system may be adapted for: (1)automatically verifying, by at least one computer processor, an identityof a particular data subject access requestor placing the first datasubject access request; and (2) at least partially in response toverifying the identity of the particular data subject access requestor,automatically processing a request, made by the particular data subjectaccess requestor, to opt out of having the organization use theparticular data subject access requestor's personal information in oneor more particular ways.

The system may, in various embodiments, be adapted for: (1) providing,by at least one computer processor, a webform creation tool that isadapted for receiving webform creation criteria from a particular user,the webform creation criteria comprising at least one criterion from agroup consisting of: (A) a language that the form will be displayed in;(B) what information is to be requested from data subjects who use thewebform to initiate a data subject access request; and (C) who any datasubject access requests that are received via the webform will be routedto; and (2) executing the webform creation tool to create both the firstwebform and the second webform.

In light of the discussion above, although the Data Subject AccessRequest Routing Module 1000 is described as being adapted to, in variousembodiments, route data subject access requests to particularindividuals for handling, it should be understood that, in particularembodiments, this module may be adapted to process at least part of, orall of, particular data subject access requests automatically (e.g.,without input from a human user). In such cases, the system may or maynot route such automatically-processed requests to a designatedindividual for additional handling or monitoring. In particularembodiments, the system may automatically fulfill all or a portion of aparticular DSAR request, automatically assign a transaction ID and/orauthentication token to the automatically fulfilled transaction, andthen display the completed DSAR transaction for display on a systemdashboard associated with a particular responsible individual that wouldotherwise have been responsible for processing the DSAR request (e.g.,an individual to whom the a webform receiving the DSAR would otherwiseroute DSAR requests). This may be helpful in allowing the human user tolater track, and answer any questions about, the automatically-fulfilledDSAR request.

It should also be understood that, although the system is described, invarious embodiments, as receiving DSAR requests via multiple webforms,each of which is located on a different website, the system may, inother embodiments, receive requests via only a single webform, orthrough any other suitable input mechanism other than a webform (e.g.,through any suitable software application, request via SMS message,request via email, data transfer via a suitable API, etc.)

In various embodiments, the system may be adapted to access informationneeded to satisfy DSAR requests via one or more suitable data models.Such data models include those that are described in greater detail inU.S. patent application Ser. No. 15/996,208, filed Jun. 1, 2018, which,as noted above, is incorporated herein by reference. In variousembodiments, the system is adapted to build and access such data modelsas described in this earlier-filed U.S. patent application.

As an example, in fulfilling a request to produce, modify, or delete,any of a data subject's personal information that is stored by aparticular entity, the system may be adapted to access a suitable datamodel to identify any personal data of the data subject that iscurrently being stored in one or more computer systems associated withthe particular entity. After using the data model to identify the data,the system may automatically process the data accordingly (e.g., bymodifying or deleting it, and/or sharing it with the DSAR requestor).

DSAR Prioritization Module

A DSAR Prioritization Module, according to various embodiments, isadapted for (1) executing the steps of receiving a data subject accessrequest; (2) at least partially in response to receiving the datasubject access request, obtaining metadata regarding a data subject ofthe data subject access request; (3) using the metadata to determinewhether a priority of the DSAR should be adjusted based on the obtainedmetadata; and (4) in response to determining that the priority of theDSAR should be adjusted based on the obtained metadata, adjusting thepriority of the DSAR.

The operation of various embodiments of the various software modulesabove is described in greater detail below. It should be understood thatthe various steps described herein may be executed, by the system, inany suitable order and that various steps may be omitted, or other stepsmay be added in various embodiments.

Operation of Example Implementation

FIGS. 3-43 are screen shots that demonstrate the operation of aparticular embodiment. FIGS. 3-6 show a graphical user interface (GUI)of an example webform construction tool. FIG. 3 shows a user working todesign a webform called “Web_form_1”. As may be understood from thevertical menu shown on the left-hand side of the screen, the webformconstruction tool allows users to design a webform by: (1) specifyingthe details of the form (via the “Form Details” tab); (2) defining thefields that will be displayed on the webform (via the “Webform Fields”tab); (3) defining the styling of the webform (via the “Form Styling”tab); and (4) defining various settings associated with the webform (viathe “Settings” tab). As shown in FIGS. 4-6, the user may also specifytext to be displayed on the webform (e.g., via a “Form Text” tab).

FIG. 4 shows that, by selecting the “Form Details” tab, the user maydefine which answers a requestor will be able to specify on the webformin response to prompts for information regarding what type of individualthey are (customer, employee, etc.) and what type of request they aremaking via the webform. Example request types include: (1) a request forall personal data that an organization is processing for the datasubject (a copy of the personal data in a commonly used,machine-readable format); (2) a request for all such personal data to bedeleted; (3) a request to update personal data that the organization isstoring for the data subject; (4) a request to opt out of having theorganization use the individual's personal information in one or moreparticular ways (e.g., per the organization's standard businesspractices); (5) file a complaint; and/or (6) other.

FIG. 5 shows that, by selecting the “Settings” tab, the user may specifyvarious system settings, such as whether Captcha will be used to verifythat information is being entered by a human, rather than a computer.

FIG. 6 shows that, by selecting the Form Styling tab, the user mayspecify the styling of the webform. The styling may include, forexample: (1) a header logo; (2) header height; (3) header color; (4)body text color; (5) body text size; (6) form label color; (7) buttoncolor; (8) button text color; (9) footer text color; (10) footer textsize; and/or any other suitable styling related to the webform.

In other embodiments, the system is configured to enable a user tospecify, when configuring a new webform, what individual at a particularorganization (e.g., company) will be responsible for responding torequests made via the webform. The system may, for example, enable theuser to define a specific default sub-organization (e.g., within theorganization) responsible for responding to DSAR's submitted via the newwebform. As such, the system may be configured to automatically route anew DSAR made via the new webform to the appropriate sub-organizationfor processing and fulfillment. In various embodiments, the system isconfigured to route one or more various portions of the DSAR to one ormore different sub-organizations within the organization for handling.

In particular embodiments, the system may include any suitable logic fordetermining how the webform routes data subject access requests. Forexample, the system may be adapted to determine which organization orindividual to route a particular data subject access request to based,at least in part, on one or more factors selected from a groupconsisting of: (1) the data subject's current location; (2) the datasubject's country of residence; (3) the type of request being made; (4)the type of systems that contain (e.g., store and/or process) the user'spersonal data (e.g., in ADP, Salesforce, etc.); or any other suitablefactor.

In particular embodiments, the system is configured to enable a usergenerating webforms to assign multiple webforms to multiple differentrespective suborganizations within an organization. For example, anorganization called ACME, Inc. may have a website for each of aplurality of different brands (e.g., sub-organizations) under which ACMEsells products (e.g., UNICORN Brand T-shirts, GRIPP Brand Jeans, etc.).As may be understood in light of this disclosure, each website for eachof the particular brands may include an associated webform forsubmitting DSAR's (either a webform directly on the web site, or onethat is accessible via a link on the website). Each respective webformmay be configured to route a DSAR made via its associated brand websiteto a particular sub-organization and/or individuals within ACME forhandling DSAR's related to the brand.

As noted above, after the user uses the webform construction tool todesign a particular webform for use on a particular web page, thewebform construction tool generates code (e.g., HTML code) that may bepasted into the particular web page to run the designed webform page. Inparticular embodiment, when pasted into the particular web page, thecode generates a selectable button on the web page that, when selected,causes the system to display a suitable DSAR request webform.

FIG. 7 shows the privacy webpage of a company (e.g., the ACMEcorporation). As shown in this figure, a requestor may submit a DSAR byselecting a “Submit a Privacy Related Request” button on the web page.

FIG. 8 shows a webform that is displayed after a requestor selects the“Submit a Privacy Related Request” button on the privacy webpage of FIG.7. As may be understood from this figure, the requestor may complete thewebform by specifying which type of user they are, and what type ofrequest they are making. The webform also asks the requestor to provideenough personal information to confirm their identity (e.g., and fulfillthe request). As shown in this figure, the system may prompt a usersubmitting a DSAR to provide information for the user such as, forexample: (1) what type of requestor the user is (e.g., employee,customer, etc.); (2) what the request involves (e.g., requesting info,opting out, deleting data, updating data, etc.); (3) first name; (4)last name; (5) email address; (6) telephone number; (7) home address;(8) one or more other pieces of identifying information; and/or (9) oneor more details associated with the request. FIG. 9 shows an examplepopulated version of the webform.

As shown in FIG. 10, after a requestor completes the webform and selectsa “submit” indicia, the system displays a message to the requestorindicating that their DSAR has been successfully submitted. The systemalso displays a Request ID associated with the request. In response tothe requestor successfully submitting the request, the system may alsosend an email (or other suitable communication) to the requestorconfirming the request. An example of a suitable confirmation email isshown in FIG. 11.

In various embodiments, the system includes a dashboard that may be usedby various individuals within an organization (e.g., one or more privacyofficers of an organization) to manage multiple DSAR requests. Asdiscussed above, the dashboard may display DSAR's submitted,respectively, to a single organization, any of multiple differentsub-organizations (divisions, departments, subsidiaries etc.) of aparticular organization, and/or any of multiple independentorganizations. For example, the dashboard may display a listing ofDSAR's that were submitted from a parent organization and from theparent organization's U.S. and European subsidiaries. This may beadvantageous, for example, because it may allow an organization tomanage all DSAR requests of all of its sub-organizations (and/or otherrelated organizations) centrally.

FIGS. 12-23, 25-27, 29-34, and 41-43 depict various exampleuser-interface screens of a DSAR request-management dashboard. As may beunderstood from FIG. 12, after an appropriate user (e.g., a privacyofficer associated with a particular organization) logs into the system,the system may display a Data Subject Request Queue that may, forexample, display a listing of all data subject access requests that theappropriate individual has been designated to process. As shown in FIG.12, each data subject access request may be represented by a respectiverow of information that includes: (1) an ID number for the request; (2)the name of the data subject who has submitted the request; (3) thestatus of the request; (4) the number of days that are left to respondto the request (e.g., according to applicable laws and/or internalprocedures); (5) an indication as to whether the deadline to respond tothe request has been extended; (6) a creation date of the request; (7)an indication of the type of requestor that submitted the request(customer, employee, etc.); (8) the name of the individual who has beenassigned to process the request (e.g., the respondent). This screen mayalso include selectable “Edit” and “Filter” buttons that respectivelyfacilitate acting on and filtering the various requests displayed on thepage.

As shown in FIG. 13, in response to a respondent selecting the editbutton while a particular DSAR is highlighted, the system displays adropdown menu allowing the respondent to select between taking thefollowing actions: (1) verify the request; (2) assign the request toanother individual; (3) request an extension; (4) reject the request; or(5) suspend the request.

FIGS. 14 and 15 show a message that the system displays to therespondent in response to the respondent selecting the “verify” option.As shown in this figure, the system prompts the respondent to indicatewhether they are sure that they wish to authenticate the request. Thesystem also presents an input field where the respondent can enter textto be displayed to the requestor along with a request for the requestorto provide information verifying that they are the data subjectassociated with the request. After the respondent populates the inputfield, they may submit the request by selecting a “Submit” button.

In particular embodiments, the input field may enable the respondent toprovide one or more supporting reasons for a decision, by therespondent, to authenticate the request. The respondent may also uploadone or more supporting documents (such as an attachment). The supportingdocuments or information may include, for example, one or more documentsutilized in confirming the requestor's identity, etc.

In response to the respondent selecting the Submit button, the systemchanges the status of the request to “In Progress” and also changes thecolor of the request's status from orange to blue (or from any othersuitable color to any different suitable color)—see FIG. 16. The systemalso generates and sends a message (e.g., an electronic or papermessage) to the requestor asking them to submit information verifyingthe request. The message may include the text that the respondententered in the text box of FIG. 14.

As shown in FIGS. 17-19, in response to a respondent selecting the“Edit” button and then selecting the “Assign” indicia from the displayeddropdown menu, the system displays a Request Assignment interface thatallows a respondent to indicate who the request should be assigned to.For example, the respondent may indicate that they will be handling therequest, or assign the request to another suitable individual, who may,for example, then be designated as the respondent for the request. Ifthe respondent assigns the request to another individual for handling,the respondent may also provide an email address or other correspondenceinformation for the individual. The Request Assignment interfaceincludes a comment box for allowing a respondent to add a message to theindividual that the assignment will be assigned to regarding theassignment. In response to the respondent selecting the “Assign” button,the system assigns the request to the designated individual forhandling. If the request has been assigned to another, designatedindividual, the system automatically generates and sends a message(e.g., an electronic message such as an email or SMS message) to thedesignated individual informing them of the assignment.

As shown in FIGS. 20-22, in response to a respondent selecting the“Edit” button and then selecting the “Reject” indicia from the displayeddropdown menu, the system displays a Reject Request interface. Thisinterface includes a comment box for allowing a respondent to add amessage to the requestor as to why the request was rejected. In responseto the respondent selecting the “Submit” button, the system changes thestatus of the request to “Rejected” and changes the color of therequest's status indicator to red (See FIG. 23). The system may alsoautomatically generate a message (e.g., an electronic or paper message)to the requestor notifying them that their request has been rejected anddisplaying the text that the respondent entered into the Reject Requestinterface of FIG. 22. An example of such a message is shown in FIG. 24.

As shown in FIGS. 25-26, in response to a respondent selecting the“Edit” button and then selecting the “Request Extension” indicia fromthe displayed dropdown menu, the system displays a Request Extensioninterface. This includes a text box for allowing a user to indicate thenumber of days for which they would like to extend the current deadlinefor responding to the request. For example, the dialog box of FIG. 26shows the respondent requesting that the current deadline be extended by90 days. In response to the respondent entering a desired extensionduration and selecting the “Submit” button, the system updates thedeadline in the system's memory (e.g., in an appropriate data structure)to reflect the extension. For instance, in the example of FIG. 26, thesystem extends the deadline to be 90 days later than the currentdeadline. As shown in FIG. 27, the system also updates the “Days Left toRespond” field within the Data Subject Request Queue to reflect theextension (e.g., from 2 days from the current date to 92 days from thecurrent date). As shown in FIG. 28, the system may also generate anappropriate message (e.g., an electronic, such as an email, or a papermessage) to the requestor indicating that the request has been delayed.This message may provide a reason for the delay and/or an anticipatedupdated completion date for the request.

In particular embodiments, the system may include logic forautomatically determining whether a requested extension complies withone or more applicable laws or internal policies and, in response,either automatically grant or reject the requested extension. Forexample, if the maximum allowable time for replying to a particularrequest is 90 days under the controlling laws and the respondentrequests an extension that would result in the fulfillment of therequest 91 or more days from the date that the request was submitted,the system may automatically reject the extension request. In variousembodiments, the system may also communicate, to the respondent (e.g.,via a suitable electronic message or text display on a system userinterface) an explanation as to why the extension request was denied,and/or a maximum amount of time (e.g., a maximum number of days) thatthe deadline may be extended under the applicable laws or policies. Invarious embodiments, if the system determines that the requestedextension is permissible under the applicable laws and/or policies, thesystem may automatically grant the extension.

In other embodiments, the system may be configured to automaticallymodify a length of the requested extension to conform with one or moreapplicable laws and/or policies. For example, if the request was for a90-day extension, but only a 60 day extension is available under theapplicable laws or regulations, the system may automatically grant a60-day extension rather than a 90 day extension. The system may beadapted to also automatically generate and transmit a suitable message(e.g., a suitable electronic or paper communication) notifying them ofthe fact that the extension was granted for a shorter, specified periodof time than requested.

As shown in FIGS. 29-34, a respondent may obtain additional detailsregarding a particular request by selecting (e.g., clicking on) therequest on the Data Subject Request Queue screen. For example, FIG. 30shows a Data Subject Request Details screen that the system displays inresponse to a respondent selecting the “Donald Blair” request on theuser interface screen of FIG. 35. As shown in FIG. 30, the Data SubjectRequest Details screen shows all correspondence between the organizationand the requesting individual regarding the selected data subject accessrequest. As may be understood from FIG. 31, when a respondent selects aparticular correspondence (e.g., email), the system displays thecorrespondence to the respondent for review or other processing.

As shown in FIG. 32, in various embodiments, the system may provide aselectable “Reply” indicia that allows the respondent to reply toparticular correspondence from an individual. As may be understood fromthis figure, in response to the respondent selecting the “Reply”indicia, the system may display a dropdown menu of various standardreplies. For example, the dropdown menu may provide the option ofgenerating a reply to the requestor indicating that the request has beenrejected, is pending, has been extended, or that the request has beencompleted.

As shown in FIG. 33, in response to the respondent selecting “Reply asCompleted”, the system may generate a draft email to the requestorexplaining that the request has been completed. The respondent may thenedit this email and send the edited correspondence (e.g., via email) tothe requestor by selecting a “Send as Complete” indicia. As shown inFIG. 34, the system may, in response, display an indicator adjacent thecorrespondence indicating that the correspondence included a replyindicating that the request was complete. This may be useful in allowingindividuals to understand the contents of the correspondence withouthaving to open it.

FIG. 35 shows an example email automatically generated by the system inresponse to the respondent selecting “Reply as Completed” on the screenshown in FIG. 32. As shown in FIG. 35, the correspondence may include asecure link that the requestor may select to access the data that wasrequested in the DSAR. In particular embodiments, the link is a link toa secure website, such as the website shown in FIG. 36, that providesaccess to the requested data (e.g., by allowing a user to download a.pdf file, or other suitable file, that includes the requested data). Asshown in FIG. 36, the website may require multiple pieces of data toverify that the requestor is permitted to access the site. For example,in order to access the website, the requestor may be required to provideboth the unique ID number of the request, and an authentication token,which the system may send to the user via email—See FIGS. 37 and 38.

FIGS. 39-43 are computer screen shots that depict additional userinterfaces according to various embodiments.

Additional Concepts

Intelligent Prioritization of DSAR's

In various embodiments, the system may be adapted to prioritize theprocessing of DSAR's based on metadata about the data subject of theDSAR. For example, the system may be adapted for: (1) in response toreceiving a DSAR, obtaining metadata regarding the data subject; (2)using the metadata to determine whether a priority of the DSAR should beadjusted based on the obtained metadata; and (3) in response todetermining that the priority of the DSAR should be adjusted based onthe obtained metadata, adjusting the priority of the DSAR.

Examples of metadata that may be used to determine whether to adjust thepriority of a particular DSAR include: (1) the type of request, (2) thelocation from which the request is being made, (3) current sensitivitiesto world events, (4) a status of the requestor (e.g., especially loyalcustomer), or (5) any other suitable metadata.

In various embodiments, in response to the system determining that thepriority of a particular DSAR should be elevated, the system mayautomatically adjust the deadline for responding to the DSAR. Forexample, the system may update the deadline in the system's memoryand/or modify the “Days Left to Respond” field (See FIG. 13) to includea fewer number of days left to respond to the request. Alternatively, orin addition, the system may use other techniques to convey to arespondent that the request should be expedited (e.g., change the colorof the request, send a message to the respondent that they shouldprocess the request before non-prioritized requests, etc.)

In various embodiments, in response to the system determining that thepriority of a particular DSAR should be lowered, the system mayautomatically adjust the deadline for responding to the DSAR by addingto the number of days left to respond to the request.

Automatic Deletion of Data Subject Records Based on Detected Systems

In particular embodiments, in response a data subject submitting arequest to delete their personal data from an organization's systems,the system may: (1) automatically determine where the data subject'spersonal data is stored; and (2) in response to determining the locationof the data (which may be on multiple computing systems), automaticallyfacilitate the deletion of the data subject's personal data from thevarious systems (e.g., by automatically assigning a plurality of tasksto delete data across multiple business systems to effectively deletethe data subject's personal data from the systems). In particularembodiments, the step of facilitating the deletion may comprise, forexample: (1) overwriting the data in memory; (2) marking the data foroverwrite; (2) marking the data as free (e.g., and deleting a directoryentry associated with the data); and/or (3) any other suitable techniquefor deleting the personal data. In particular embodiments, as part ofthis process, the system uses an appropriate data model (see discussionabove) to efficiently determine where all of the data subject's personaldata is stored.

Automatic Determination of Business Processes that Increase Chance ofDeletion Requests

In various embodiments, the system is adapted to store, in memory, a logof DSAR actions. The system may also store, in memory, additionalinformation regarding the data subjects of each of the requests. Thesystem may use this information, for example, to determine whichbusiness processes are most commonly associated with a data subjectsubmitting a request to have their personal information deleted from theorganization's systems. The organization may then use this informationto revise the identified business processes in an effort to reduce thenumber of deletion requests issued by data subjects associated with thebusiness processes.

As a particular example, the system may analyze stored information todetermine that a high number (e.g., 15%) of all participants in acompany's loyalty program submit requests to have their personalinformation deleted from the company's systems. In response to makingthis determination, the system may issue an electronic alert to anappropriate individual (e.g., a privacy officer of the company),informing them of the high rate of members of the company's loyaltyprogram issuing personal data delete requests. This alert may prompt theindividual to research the issue and try to resolve it.

Automated Data Subject Verification

In various embodiments, before a data subject request can be processed,the data subject's identity may need to be verified. In variousembodiments, the system provides a mechanism to automatically detect thetype of authentication required for a particular data subject based onthe type of Data Subject Access Request being made and automaticallyissues a request to the data subject to verify their identity againstthat form of identification. For example, a subject rights request mightonly require two types of authentication, but a deletion request mayrequire four types of data to verify authentication. The system mayautomatically detect which is type of authentication is required basedon the DSAR and send an appropriate request to the data subject toverify their identity.

Stated more particularly, when processing a data subject access request,the system may be configured to verify an identity of the data subjectprior to processing the request (e.g., or as part of the processingstep). In various embodiments, confirming the identity of the datasubject may, for example, limit a risk that a third-party or otherentity may gain unlawful or unconsented to access to the requestor'spersonal data. The system may, for example, limit processing andfulfillment of requests relating to a particular data subject torequests that are originated by (e.g., received from) the particulardata subject. When processing a data subject access request, the systemmay be configured to use various reasonable measures to verify theidentity of the data subject who requests access (e.g., in particular inthe context of online services and online identifiers). In particularembodiments, the system is configured to substantially automaticallyvalidate an identity of a data subject when processing the data subjectaccess request.

For example, in particular embodiments, the system may be configured tosubstantially automatically (e.g., automatically) authenticate and/orvalidate an identity of a data subject using any suitable technique.These techniques may include, for example: (1) one or more credit-basedand/or public- or private-information-based verification techniques; (2)one or more company verification techniques (e.g., in the case of abusiness-to-business data subject access request); (3) one or moretechniques involving integration with a company's employeeauthentication system; (4) one or more techniques involving a company's(e.g., organization's) consumer portal authentication process; (5) etc.Various exemplary techniques for authenticating a data subject arediscussed more fully below.

In particular embodiments, when authenticating a data subject (e.g.,validating the data subject's identity), the system may be configured toexecute particular identity confirmation steps, for example, byinterfacing with one or more external systems (e.g., one or morethird-party data aggregation systems). For example, the system, whenvalidating a data subject's identity, may begin by verifying that aperson with the data subject's name, address, social security number, orother identifying characteristic (e.g., which may have been provided bythe data subject as part of the data subject access request) actuallyexists. In various embodiments, the system is configured to interfacewith (e.g., transmit a search request to) one or more credit reportingagencies (e.g., Experian, Equifax, TransUnion, etc.) to confirm that aperson with one or more characteristics provided by the data subjectexists. The system may, for example, interface with such creditreporting agencies via a suitable plugin (e.g., software plugin).Additionally, there might be a verification on behalf of a trustedthird-party system (e.g., the controller).

In still other embodiments, the system may be configured to utilize oneor more other third-party systems (e.g., such as LexisNexis, IDology,RSA, etc.), which may, for example, compile utility and phone bill data,property deeds, rental agreement data, and other public records forvarious individuals. The system may be configured to interface with oneor more such third-party systems to confirm that a person with one ormore characteristics provided by the data subject exists.

After the step of confirming the existence of a person with the one ormore characteristics provided by the data subject, the system may beconfigured to confirm that the person making the data subject accessrequest is, in fact, the data subject. The system may, for example,verify that the requestor is the data subject by prompting the requestorto answer one or more knowledge-based authentication questions (e.g.,out-of-wallet questions). In particular embodiments, the system isconfigured to utilize one or more third-party services as a source ofsuch questions (e.g., any of the suitable third-party sources discussedimmediately above). The system may use third-party data from the one ormore third-party sources to generate one or more questions. These one ormore questions may include questions that a data subject should know ananswer to without knowing the question ahead of time (e.g., one or moreprevious addresses, a parent or spouse name and/or maiden name, etc.).

FIG. 46 depicts an exemplary identity verification questionnaire. As maybe understood from this figure, an identity verification questionnairemay include one or more questions whose responses include data that thesystem may derive from one or more credit agencies or other third-partydata aggregation services (e.g., such as previous street addresses,close associates, previous cities lived in, etc.). In particularembodiments, the system is configured to provide these one or morequestions to the data subject in response to receiving the data subjectaccess request. In other embodiments, the system is configured to promptthe data subject to provide responses to the one or more questions at alater time (e.g., during processing of the request). In particular otherembodiments, the system is configured to substantially automaticallycompare one or more pieces of information provided as part of the datasubject access request to one or more pieces of data received from athird-party data aggregation service in order to substantiallyautomatically verify the requestor's identity.

In still other embodiments, the system may be configured to prompt arequestor to provide one or more additional pieces of information inorder to validate the requestor' s identity. This information mayinclude, for example: (1) at least a portion of the requestor's socialsecurity number (e.g., last four digits); (2) a name and/or place ofbirth of the requestor's father; (3) a name, maiden name, and/or placeof birth of the requestor' s mother; and/or (4) any other informationwhich may be useful for confirming the requestor' s identity (e.g., suchas information available on the requestor' s birth certificate). Inother embodiments, the system may be configured to prompt the requestorto provide authorization for the company to check the requestor' ssocial security or other private records (e.g., credit checkauthorization, etc.) to obtain information that the system may use toconfirm the requestor's identity. In other embodiments, the system mayprompt the user to provide one or more images (e.g., using a suitablemobile computing device) of an identifying document (e.g., a birthcertificate, social security card, driver's license, etc.).

The system may, in response to a user providing one or more responsesthat matches information that the system receives from one or morethird-party data aggregators or through any other suitable background,credit, or other search, substantially automatically authenticate therequestor as the data subject. The system may then continue processingthe data subject's request, and ultimately fulfill their request.

In particular embodiments, such as embodiments in which the requestorincludes a business (e.g., as in a business to business data subjectaccess request), the system may be configured to authenticate therequesting business using one or more company verification techniques.These one or more company validation techniques may include, forexample, validating a vendor contract (e.g., between the requestingbusiness and the company receiving the data subject access request);receiving a matching token, code, or other unique identifier provided bythe company receiving the data subject access request to the requestingbusiness; receiving a matching file in possession of both the requestingbusiness and the company receiving the data subject access request;receiving a signed contract, certificate (e.g., digital or physical), orother document memorializing an association between the requestingbusiness and the company receiving the data subject access request;and/or any other suitable method of validating that a particular requestis actually made on behalf of the requesting business (e.g., byrequesting the requesting business to provide one or more pieces ofinformation, one or more files, one or more documents, etc. that mayonly be accessible to the requesting business).

In other embodiments, the system may be configured to authenticate arequest via integration with a company's employee or customer (e.g.,consumer) authentication process. For example, in response to receivinga data subject access request that indicates that the data subject is anemployee of the company receiving the data subject access request, thesystem may be configured to prompt the employee to login to thecompany's employee authentication system (e.g., Okta, Azure, AD, etc.)In this way, the system may be configured to authenticate the requestorbased at least in part on the requestor successfully logging into theauthentication system using the data subject's credentials. Similarly,in response to receiving a data subject access request that indicatesthat the data subject is a customer of the company receiving the datasubject access request, the system may be configured to prompt thecustomer to login to an account associated with the company (e.g., via aconsumer portal authentication process). In a particular example, thismay include, for example, an Apple ID (for data subject access requestsreceived by Apple). In this way, the system may be configured toauthenticate the requestor based at least in part on the requestorsuccessfully logging into the authentication system using the datasubject's credentials. In some embodiments, the system may be configuredto require the requestor to login using two-factor authentication orother suitable existing employee or consumer authentication process.

Data Subject Blacklist

In various embodiments, a particular organization may not be required torespond to a data subject access request that originates (e.g., isreceived from) a malicious requestor. A malicious requestor may include,for example: (1) a requestor (e.g., an individual) that submitsexcessive or redundant data subject access requests; (2) a group ofrequestors such as researchers, professors, students, NGOs, etc. thatsubmit a plurality of requests for reasons other than those reasonsprovided by policy, law, etc.; (3) a competitor of the company receivingthe data subject access request that is submitting such requests to tieup the company's resources unnecessarily; (4) a terrorist or otherorganization that may spam requests to disrupt the company's operationand response to valid requests; and/or (5) any other request that mayfall outside the scope of valid requests made for reasons proscribed bypublic policy, company policy, or law. In particular embodiments, thesystem is configured to maintain a blacklist of such maliciousrequestors.

In particular embodiments, the system is configured to track a source ofeach data subject access request and analyze each source to identifysources from which: (1) the company receives a large volume of requests;(2) the company receives a large number of repeat requests; (3) etc.These sources may include, for example: (1) one or more particular IPaddresses; (2) one or more particular domains; (3) one or moreparticular countries; (4) one or more particular institutions; (5) oneor more particular geographic regions; (6) etc. In various embodiments,in response to analyzing the sources of the requests, the system mayidentify one or more sources that may be malicious (e.g., are submittingexcessive requests).

In various embodiments, the system is configured to maintain a databaseof the identified one or more sources (e.g., in computer memory). Inparticular embodiments, the database may store a listing of identities,data sources, etc. that have been blacklisted (e.g., by the system). Inparticular embodiments, the system is configured to, in response toreceiving a new data subject access request, cross reference the requestwith the blacklist to determine if the requestor is on the blacklist oris making the request from a blacklisted source. The system may then, inresponse to determining that the requestor or source is blacklisted,substantially automatically reject the request. In particularembodiments, the blacklist cross-referencing step may be part of therequestor authentication (e.g., verification) discussed above. Invarious embodiments, the system may be configured to analyze requestdata on a company by company basis to generate a blacklist. In otherembodiments, the system may analyze global data (e.g., all datacollected for a plurality of companies that utilize the data subjectaccess request fulfillment system) to generate the blacklist.

In particular embodiments, the system may be configured to fulfill datasubject access requests for the purpose of providing a data subject withinformation regarding what data the company collects and for whatpurpose, for example, so the data subject can ensure that the company iscollecting data for lawful reasons. As such, the system may beconfigured to identify requestors and other sources of data requeststhat are made for other reasons (e.g., one or more reasons that wouldnot obligate the company to respond to the request). These reasons mayinclude, for example, malicious or other reasons such as: (1) researchby an academic institution by one or more students or professors; (2)anticompetitive requests by one or more competitors; (3) requests bydisgruntled former employees for nefarious reasons; (4) etc.

In particular embodiments, the system may, for example, maintain adatabase (e.g., in computer memory) of former employees. In otherembodiments, the system may, for example: (1) identify a plurality of IPaddresses associated with a particular entity (e.g., academicorganization, competitor, etc.); and (2) substantially automaticallyreject a data subject access request that originates from the pluralityof IP addresses. In such embodiments, the system may be configured toautomatically add such identified IP addresses and/or domains to theblacklist.

In still other embodiments, the system is configured to maintain alisting of blacklisted names of particular individuals. These mayinclude, for example, one or more individuals identified (e.g., by anorganization or other entity) as submitting malicious data subjectaccess requests).

FIG. 47 depicts a queue of pending data subject access requests. Asshown in this figure, the first three listed data subject accessrequests are new and require verification before processing andfulfillment can begin. As shown in this figure, a user (e.g., such as aprivacy officer or other privacy controller) may select a particularrequest, and select an indicia for verifying the request. The user mayalso optionally select to reject the request. FIG. 48 depicts anauthentication window that enables the user to authenticate a particularrequest. In various embodiments, the user may provide an explanation ofwhy the user is authenticating the request (e.g., because the requestorsuccessfully completed on or more out-of-wallet questions or for anyother suitable reason). The user may further submit one or moreattachments to support the verification. In this way, the system may beconfigured to document that the authentication process was performed foreach request (e.g., in case there was an issue with improperlyfulfilling a request, the company could show that they are followingprocedures to prevent such improper processing). In other embodiments,the system may enable the user to provide similar support when rejectinga request (e.g., because the requestor was blacklisted, made excessiverequests, etc.).

Data Subject Access Request Fulfillment Cost Determination

In various embodiments, as may be understood in light of thisdisclosure, fulfilling a data subject access request may be particularlycostly. In some embodiments, a company may store data regarding aparticular data subject in multiple different locations for a pluralityof different reasons as part of a plurality of different processing andother business activities. For example, a particular data subject may beboth a customer and an employee of a particular company or organization.Accordingly, in some embodiments, fulfilling a data subject accessrequest for a particular data subject may involve a plurality ofdifferent information technology (IT) professionals in a plurality ofdifferent departments of a particular company or organization. As such,it may be useful to determine a cost of a particular data subject accessrequest (e.g., particularly because, in some cases, a data subject isentitled to a response to their data subject access request as a matterof right at no charge).

In particular embodiments, in response to receiving a data subjectaccess request, the system may be configured to: (1) assign the requestto at least one privacy team member; (2) identify one or more IT teamsrequired to fulfill the request (e.g., one or more IT teams associatedwith one or more business units that may store personal data related tothe request); (3) delegate one or more subtasks of the request to eachof the one or more IT teams; (4) receive one or more time logs from eachindividual involved in the processing and fulfillment of the datasubject access request; (5) calculate an effective rate of eachindividual's time (e.g., based at least in part on the individual'ssalary, bonus, benefits, chair cost, etc.); (6) calculate an effectivecost of fulfilling the data subject access request based at least inpart on the one or more time logs and effective rate of each of theindividual's time; and (7) apply an adjustment to the calculatedeffective cost that accounts for one or more external factors (e.g.,overhead, etc.) in order to calculate a cost of fulfilling the datasubject access request.

In particular embodiments, the system is configured to substantiallyautomatically track an amount of time spent by each individual involvedin the processing and fulfillment of the data subject access request.The system may, for example, automatically track an amount of timebetween each individual opening and closing a ticket assigned to them aspart of their role in processing or fulfilling the data subject accessrequest. In other embodiments, the system may determine the time spentbased on an amount of time provided by each respective individual (e.g.,the individual may track their own time and submit it to the system).

In various embodiments, the system is configured to measure a cost ofeach particular data subject access request received, and analyze one ormore trends in costs of, for example: (1) data subject access requestsover time; (2) related data subject access requests; (3) etc. Forexample, the system may be configured to track and analyze cost andtime-to-process trends for one or more social groups, one or morepolitical groups, one or more class action groups, etc. In particular,the system may be configured to identify a particular group from whichthe system receives particularly costly data subject access request(e.g., former and/or current employees, members of a particular socialgroup, members of a particular political group, etc.).

In particular embodiments, the system may be configured to utilize datasubject access request cost data when processing, assigning, and/orfulfilling future data subject access requests (e.g., from a particularidentified group, individual, etc.). For example, the system may beconfigured to prioritize requests that are expected to be less costlyand time-consuming (e.g., based on past cost data) over requestsidentified as being likely more expensive. Alternatively, the system mayprioritize more costly and time-consuming requests over less costly onesin the interest of ensuring that the system is able to respond to eachrequest in a reasonable amount of time (e.g., within a time required bylaw, such as a thirty day period, or any other suitable time period).

Customer Satisfaction Integration with Data Subject Access Requests

In various embodiments, the system may be configured to collect customersatisfaction data, for example: (1) as part of a data subject accessrequest submission form; (2) when providing one or more results of adata subject access request to the data subject; or (3) at any othersuitable time. In various embodiments, the customer satisfaction datamay be collected in the form of a suitable survey, free-form responsequestionnaire, or other suitable satisfaction data collection format(e.g., thumbs up vs. thumbs down, etc.).

FIG. 49 depicts an exemplary customer satisfaction survey that may beincluded as part of a data subject access request form, provided alongwith the results of a data subject access request, provided in one ormore messages confirming receipt of a data subject access request, etc.As shown in the figure, the customer satisfaction survey may relate tohow likely a customer (e.g., a data subject) is to recommend the company(e.g., to which the data subject has submitted the request) to a friend(e.g., or colleague). In the example shown in FIG. 49, the satisfactionsurvey may relate to a Net Promoter score (NPS), which may indicate aloyalty of a company's customer relationships. Generally speaking, theNet Promoter Score may measure a loyalty that exists between a providerand a consumer. In various embodiments, the provider may include acompany, employer, or any other entity. In particular embodiments, theconsumer may include a customer, employee, or other respondent to an NPSsurvey.

In particular embodiments, the question depicted in FIG. 49 is theprimary question utilized in calculating a Net Promoter Score (e.g.,“how likely is it that you would recommend our company/product/serviceto a friend or colleague?”). In particular embodiments, the question ispresented with responses ranging from 0 (not at all likely) to 10(extremely likely). In particular embodiments, the question may includeany other suitable scale. As may be understood from FIG. 49, the systemmay be configured to assign particular categories to particular ratingson the 10 point scale. The system may be configured to track and storeresponses provided by consumers and calculate an overall NPS score forthe provider. The system may be further configured to generate a visualrepresentation of the NPS score, including a total number of responsesreceived for each particular score and category as shown in FIG. 49.

In various embodiments, the system may be configured to measure datarelated to any other suitable customer satisfaction method (e.g., inaddition to NPS). By integrating a customer satisfaction survey with thedata subject access request process, the system may increase a number ofconsumers that provide one or more responses to the customersatisfaction survey. In particular embodiments, the system is configuredto require the requestor to respond to the customer satisfaction surveyprior to submitting the data subject access request.

Identifying and Deleting Orphaned Data

In particular embodiments, an Orphaned Data Action System is configuredto analyze one or more data systems (e.g., data assets), identify one ormore pieces of personal data that are one or more pieces of personaldata that are not associated with one or more privacy campaigns of theparticular organization, and notify one or more individuals of theparticular organization of the one or more pieces of personal data thatare one or more pieces of personal data that are not associated with oneor more privacy campaigns of the particular organization. In variousembodiments, one or more processes described herein with respect to theorphaned data action system may be performed by any suitable server,computer, and/or combination of servers and computers.

Various processes performed by the Orphaned Data Action System may beimplemented by an Orphaned Data Action Module 5000. Referring to FIG.50, in particular embodiments, the system, when executing the OrphanedData Action Module 5000, is configured to: (1) access one or more dataassets of a particular organization; (2) scan the one or more dataassets to generate a catalog of one or more privacy campaigns and one ormore pieces of personal information associated with one or moreindividuals; (3) store the generated catalog in computer memory; (4)scan one or more data assets based at least in part on the generatedcatalog to identify a first portion of the one or more pieces ofpersonal data that are one or more pieces of personal data that are notassociated with the one or more privacy campaigns; (5) generate anindication that the first portion of one or more pieces of personal datathat are not associated with the one or more privacy campaigns of theparticular organization is to be removed from the one or more dataassets; (6) present the indication to one or more individuals associatedwith the particular organization; and (7) remove the first portion ofthe one or more pieces of personal data that are not associated with theone or more privacy campaigns of the particular organization from theone or more data assets.

When executing the Orphaned Data Action Module 5000, the system begins,at Step 5010, by accessing one or more data systems associated with theparticular entity. The particular entity may include, for example, aparticular organization, company, sub-organization, etc. In particularembodiments, the one or more data assets (e.g., data systems) mayinclude, for example, any entity that collects, processes, contains,and/or transfers data (e.g., a software application, “internet ofthings” computerized device, database, website, data-center, server,etc.). For example, a data asset may include any software or deviceutilized by a particular entity for data collection, processing,transfer, storage, etc.

In particular embodiments, the system is configured to identify andaccess the one or more data assets using one or more data modelingtechniques. As discussed more fully above, a data model may store thefollowing information: (1) the entity that owns and/or uses a particulardata asset; (2) one or more departments within the organization that areresponsible for the data asset; (3) one or more software applicationsthat collect data (e.g., personal data) for storage in and/or use by thedata asset; (4) one or more particular data subjects (or categories ofdata subjects) that information is collected from for use by the dataasset; (5) one or more particular types of data that are collected byeach of the particular applications for storage in and/or use by thedata asset; (6) one or more individuals (e.g., particular individuals ortypes of individuals) that are permitted to access and/or use the datastored in, or used by, the data asset; (7) which particular types ofdata each of those individuals are allowed to access and use; and (8)one or more data assets (destination assets) that the data istransferred to for other use, and which particular data is transferredto each of those data assets.

As may be understood in light of this disclosure, the system may utilizea data model (e.g., or one or more data models) of data assetsassociated with a particular entity to identify and access the one ormore data assets associated with the particular entity.

Continuing to Step 5020, the system is configured to scan the one ormore data assets to generate a catalog of one or more privacy campaignsand one or more pieces of personal information associated with one ormore individuals. The catalog may include a table of the one or moreprivacy campaigns within the data assets of the particular entity and,for each privacy campaign, the one or more pieces of personal datastored within the data assets of the particular entity that areassociated with the particular privacy campaign. In any embodimentdescribed herein, personal data may include, for example: (1) the nameof a particular data subject (which may be a particular individual); (2)the data subject's address; (3) the data subject's telephone number; (4)the data subject's e-mail address; (5) the data subject's socialsecurity number; (6) information associated with one or more of the datasubject's credit accounts (e.g., credit card numbers); (7) bankinginformation for the data subject; (8) location data for the data subject(e.g., their present or past location); (9) internet search history forthe data subject; and/or (10) any other suitable personal information,such as other personal information discussed herein.

In some implementations, the system may access, via one or more computernetworks, one or more data models that map an association between one ormore pieces of personal data stored within one or more data assets ofthe particular entity and one or more privacy campaigns of theparticular entity. As further described herein, the data models mayaccess the data assets of the particular entity and use one or moresuitable data mapping techniques to link, or otherwise associate, theone or more pieces of personal data stored within one or more dataassets of the particular entity and one or more privacy campaigns of theparticular entity. In some implementations, the one or more data modelsmay link, or otherwise associate, a particular individual and each pieceof personal data of that particular individual that is stored on one ormore data assets of the particular entity.

In some embodiments, the system is configured to generate and populate adata model based at least in part on existing information stored by thesystem (e.g., in one or more data assets), for example, using one ormore suitable scanning techniques. In still other embodiments, thesystem is configured to access an existing data model that maps personaldata stored by one or more organization systems to particular associatedprocessing activities. In some implementations, the system is configuredto generate and populate a data model substantially on the fly (e.g., asthe system receives new data associated with particular processingactivities). For example, a particular processing activity (e.g.,privacy campaign) may include transmission of a periodic advertisinge-mail for a particular company (e.g., a hardware store). A data modelmay locate the collected and stored email addresses for customers thatelected to receive (e.g., consented to receipt of) the promotional emailwithin the data assets of the particular entity, and then map each ofthe stored email addresses to the particular processing activity (i.e.,the transmission of a periodic advertising e-mail) within the dataassets of the particular entity.

Next, at Step 5030, the system is configured to store the generatedcatalog of one or more privacy campaigns and one or more pieces ofpersonal information associated with one or more individuals. In someimplementations, the system may receive an indication that a newprocessing activity (e.g., privacy campaign) has been launched by theparticular entity. In response to receiving the indication, the systemmay modify the one or more data models to map an association between (i)one or more pieces of personal data associated with one or moreindividuals obtained in connection with the new privacy campaign and(ii) the new privacy campaign initiated by the particular entity. As thesystem receives one or more pieces of personal data associated with oneor more individuals (e.g., an email address signing up to receiveinformation from the particular entity), then the data model associatedwith the particular processing activity may associate the receivedpersonal data with the privacy campaign. In some implementations, one ormore data assets may already include the particular personal data (e.g.,email address) because the particular individual, for example,previously provided their email address in relation to a differentprivacy campaign of the particular entity. In response, the system mayaccess the particular personal data and associate that particularpersonal data with the new privacy campaign.

At Step 5040, the system is configured to scan one or more data assetsbased at least in part on the generated catalog to identify a firstportion of the one or more pieces of personal data that are one or morepieces of personal data that are not associated with the one or moreprivacy campaigns. In various embodiments, the system may use thegenerated catalogue to scan the data assets of the particular entity toidentify personal data that has been collected and stored using one ormore computer systems operated and/or utilized by a particularorganization where the personal data is not currently being used as partof any privacy campaigns, processing activities, etc. undertaken by theparticular organization. The one or more pieces of personal data thatare not associated with the one or more privacy campaigns may be aportion of the personal data that is stored by the particular entity. Insome implementations, the system may analyze the data models to identifythe one or more pieces of personal data that are not associated with theone or more privacy campaigns.

When the particular privacy campaign, processing activity, etc. isterminated or otherwise discontinued, the system may determine if any ofthe associated personal data that has been collected and stored by theparticular organization is now orphaned data. In some implementations,in response to the termination of a particular privacy campaign and/orprocessing activity, (e.g., manually or automatically), the system maybe configured to scan one or more data assets based at least in part onthe generated catalog or analyze the data models to determine whetherany of the personal data that has been collected and stored by theparticular organization is now orphaned data (e.g., whether any personaldata collected and stored as part of the now-terminated privacy campaignis being utilized by any other processing activity, has some other legalbasis for its continued storage, etc.). In some implementations, thesystem may generate an indication that one or more pieces of personaldata that are associated with the terminated one or more privacycampaigns are included in the portion of the one or more pieces ofpersonal data (e.g., orphaned data).

In additional implementations, the system may determine that aparticular privacy campaign, processing activity, etc. has not beenutilized for a period of time (e.g., a day, a month, a year). Inresponse, the system may be configured to terminate the particularprocessing activity, processing activity, etc. In some implementations,in response to the system determining that a particular processingactivity has not been utilized for a period of time, the system mayprompt one or more individuals associated with the particular entity toindicate whether the particular privacy campaign should be terminated orotherwise discontinued.

For example, a particular processing activity may include transmissionof a periodic advertising e-mail for a particular company (e.g., ahardware store). As part of the processing activity, the particularcompany may have collected and stored e-mail addresses for customersthat elected to receive (e.g., consented to the receipt of) thepromotional e-mails. In response to determining that the particularcompany has not sent out any promotional e-mails for at least aparticular amount of time (e.g., for at least a particular number ofmonths), the system may be configured to: (1) automatically terminatethe processing activity; (2) identify any of the personal data collectedas part of the processing activity that is now orphaned data (e.g., thee-mail addresses); and (3) automatically delete the identified orphaneddata. The processing activity may have ended for any suitable reason(e.g., because the promotion that drove the periodic e-mails has ended).As may be understood in light of this disclosure, because the particularorganization no longer has a valid basis for continuing to store thee-mail addresses of the customers once the e-mail addresses are nolonger being used to send promotional e-mails, the organization may wishto substantially automate the removal of personal data stored in itscomputer systems that may place the organization in violation of one ormore personal data storage rules or regulations.

Continuing to Step 5050, the system is configured to generate anindication that the portion of one or more pieces of personal data thatare not associated with the one or more privacy campaigns of theparticular entity is to be removed from the one or more data assets. AtStep 5060, the system is configured to present the indication to one ormore individuals associated with the particular entity. The indicationmay be an electronic notification to be provided to an individual (e.g.,privacy officer) associated with the particular entity. The electronicnotification may be, for example, (1) a notification within a softwareapplication (e.g., a data management system for the one or more dataassets of the particular entity), (2) an email notification, (3) etc.

In some implementations, the indication may enable the individual (e.g.,privacy officer of the particular entity) to select a set of the one ormore pieces of personal data of the portion of the one or more pieces ofpersonal data to retain based on one or more bases to retain the set ofthe one or more pieces of personal data.

In particular embodiments, the system may prompt the one or moreindividuals to provide one or more bases to retain the first set of theone or more pieces of personal data of the first portion of the one ormore pieces of personal data that are not associated with the one ormore privacy campaigns. In some implementations, in response toreceiving the provided one or more valid bases to retain the first setof the one or more pieces of personal data from the one or moreindividuals associated with the particular entity, submitting theprovided one or more valid bases to retain the first set of the one ormore pieces of personal data to one or more second individualsassociated with the particular entity for authorization. In response,the system may retain the first set of the one or more pieces ofpersonal data of the first portion of the one or more pieces of personaldata from the one or more individuals associated with the particularentity. Further, the system may remove a second set of the one or morepieces of personal data of the first portion of the one or more piecesof personal data that are not associated with the one or more privacycampaigns from the one or more data assets. In particular embodiments,the second set of the one or more pieces of personal data may bedifferent from the first set of the one or more pieces of personal data.

Continuing to Step 5070, the system is configured to remove, by one ormore processors, the first portion of the one or more pieces of personaldata that are not associated with the one or more privacy campaigns ofthe particular entity from the one or more data assets.

Data Testing to Confirm Deletion under a Right to Erasure

In particular embodiments, a Personal Data Deletion System is configuredto: (1) at least partially automatically identify and delete personaldata that an entity is required to erase under one or more of theconditions discussed above; and (2) perform one or more data tests afterthe deletion to confirm that the system has, in fact, deleted anypersonal data associated with the data subject.

Various processes performed by the Personal Data Deletion System may beimplemented by a Personal Data Deletion and Testing Module 5100.Referring to FIG. 51, in particular embodiments, the system, whenexecuting the Personal Data Deletion and Testing Module 5100, isconfigured to: (1) receive an indication that the entity has completedan erasure of one or more pieces of personal data associated with thedata subject under a right of erasure; (2) initiate a test interactionbetween the data subject and the entity, the test interaction requiringa response from the entity to the data subject; (3) determine whetherone or more system associated with the entity have initiated a testinteraction response to the data subject based at least in part on thetest interaction; (4) in response to determining that the one or moresystems associated with the entity have initiated the test interactionresponse, (a) determine that the entity has not completed the erasure ofthe one or more pieces of personal data associated with the data subjectand (b) automatically take one or more actions with regard to thepersonal data associated with the data subject.

When executing the Personal Data Deletion and Testing Module 5100, thesystem begins, at Step 5110, by receiving an indication that the entityhas completed an erasure of one or more pieces of personal dataassociated with the data subject under a right of erasure. Theparticular entity may include, for example, a particular organization,company, sub-organization, etc. In particular embodiments, the one ormore computers systems may be configured to store (e.g., in memory) anindication that the data subject's request to delete any of theirpersonal data stored by the one or more computers systems has beenprocessed. Under various legal and industry policies/standards, theorganization may have a certain period of time (e.g., a number of days)in order to comply with the one or more requirements related to thedeletion or removal of personal data in response to receiving a requestfrom the data subject or in response to identifying one or more of theconditions requiring deletion discussed above. In response to thereceiving an indication that the deletion request for the data subject'spersonal data has been processed or the certain period of time(described above) has passed, the system may be configured to perform adata test to confirm the deletion of the data subject's personal data.

Continuing to Step 5120, in response to receiving the indication thatthe entity has completed the erasure, the system is configured toinitiate a test interaction between the data subject and the entity, thetest interaction requiring a response from the entity to the datasubject. In particular embodiments, when performing the data test, thesystem may be configured to provide an interaction request to the entityon behalf of the data subject. In particular embodiments, theinteraction request may include, for example, a request for one or morepieces of data associated with the data subject (e.g., accountinformation, etc.). In various embodiments, the interaction request is arequest to contact the data subject (e.g., for any suitable reason). Thesystem may, for example, be configured to substantially automaticallycomplete a contact-request form (e.g., a webform made available by theentity) on behalf of the data subject. In various embodiments, whenautomatically completing the form on behalf of the data subject, thesystem may be configured to only provide identifying data, but not toprovide any contact data. In response to submitting the interactionrequest (e.g., submitting the webform), the system may be configured todetermine whether the one or more computers systems have generatedand/or transmitted a response to the data subject. The system may beconfigured to determine whether the one or more computers systems havegenerated and/or transmitted the response to the data subject by, forexample, analyzing one or more computer systems associated with theentity to determine whether the one or more computer systems havegenerated a communication to the data subject (e.g., automatically) fortransmission to an e-mail address or other contact method associatedwith the data subject, generated an action-item for an individual tocontact the data subject at a particular contact number, etc.

To perform the data test, for example, the system may be configured to:(1) access (e.g., manually or automatically) a form for the entity(e.g., a web-based “Contact Us” form); (2) input a unique identifierassociated with the data subject (e.g., a full name or customer IDnumber) without providing contact information for the data subject(e.g., mailing address, phone number, email address, etc.); and (3)input a request, within the form, for the entity to contact the datasubject to provide information associated with the data subject (e.g.,the data subject's account balance with the entity). In response tosubmitting the form to the entity, the system may be configured todetermine whether the data subject is contacted (e.g., via a phone callor email) by the one or more computers systems (e.g., automatically). Insome implementations, completing the contact-request form may includeproviding one or more pieces of identifying data associated with thedata subject, the one or more pieces of identifying data comprising dataother than contact data. In response to determining that the datasubject has been contacted following submission of the form, the systemmay determine that the one or more computers systems have not fullydeleted the data subject's personal data (e.g., because the one or morecomputers systems must still be storing contact information for the datasubject in at least one location).

In particular embodiments, the system is configured to generate one ormore test profiles for one or more test data subjects. For each of theone or more test data subjects, the system may be configured to generateand store test profile data such as, for example: (1) name; (2) address;(3) telephone number; (4) e-mail address; (5) social security number;(6) information associated with one or more credit accounts (e.g.,credit card numbers); (7) banking information; (8) location data; (9)internet search history; (10) non-credit account data; and/or (11) anyother suitable test data. The system may then be configured to at leastinitially consent to processing or collection of personal data for theone or more test data subjects by the entity. The system may thenrequest deletion of data of any personal data associated with aparticular test data subject. In response to requesting the deletion ofdata for the particular test data subject, the system may then take oneor more actions using the test profile data associated with theparticular test data subjects in order to confirm that the one or morecomputers systems have, in fact, deleted the test data subject'spersonal data (e.g., any suitable action described herein). The systemmay, for example, be configured to: (1) initiate a contact request onbehalf of the test data subject; (2) attempt to login to one or moreuser accounts that the system had created for the particular test datasubject; and/or (3) take any other action, the effect of which couldindicate a lack of complete deletion of the test data subject's personaldata.

Next, at Step 5130, in response to initiating the test interaction, thesystem is configured to determine whether one or more system associatedwith the entity have initiated a test interaction response to the datasubject based at least in part on the test interaction. In response todetermining that the entity has generated a response to the testinteraction, the system may be configured to determine that the entityhas not complied with the data subject's request (e.g., deletion oftheir personal data from the one or more computers systems). Forexample, if the test interaction requests for the entity to locate andprovide any personal data the system has stored related to the datasubject, then by the system providing a response that includes one ormore pieces of personal data related to the data subject, the system maydetermine that the one or more computers systems have not complied withthe request. As described above, the request may be an erasure of one ormore pieces of personal data associated with the data subject under aright of erasure. In some implementations, the test interaction responsemay be any response that includes any one of the one or more pieces ofpersonal data the system indicated was erased under the right oferasure. In some implementations, the test interaction response may notinclude response that indicates that the one or more pieces of personaldata the system indicated was erased under the right of erasure was notfound or accessed by the system.

At Step 5140, in response to determining that the one or more systemsassociated with the entity have initiated the test interaction responsethe system is configured to (a) determine that the one or more computerssystems have not completed the erasure of the one or more pieces ofpersonal data associated with the data subject, and (b) automaticallytake one or more actions with regard to the personal data associatedwith the data subject. In response to determining that the one or morecomputers systems have not fully deleted a data subject's (e.g., or testdata subject's) personal data, the system may then be configured, inparticular embodiments, to: (1) flag the data subject's personal datafor follow up by one or more privacy officers to investigate the lack ofdeletion; (2) perform one or more scans of one or more computing systemsassociated with the entity to identify any residual personal data thatmay be associated with the data subject; (3) generate a reportindicating the lack of complete deletion; and/or (4) take any othersuitable action to flag the data subject, personal data, initial requestto be forgotten, etc. for follow up.

In various embodiments, the one or more actions may include: (1)identifying the one or more pieces of personal data associated with thedata subject that remain stored in the one or more computer systems ofthe entity; (2) flagging the one or more pieces of personal dataassociated with the data subject that remain stored in the one or morecomputer systems of the entity; and (3) providing the flagged one ormore pieces of personal data associated with the data subject thatremain stored in the one or more computer systems of the entity to anindividual associated with the entity.

In various embodiments, the system may monitor compliance by aparticular entity with a data subject's request to delete the datasubject's personal data from the one or more computers systemsassociated with a particular entity. The system may, for example, beconfigured to test to ensure the data has been deleted by: (1)submitting a unique token of data through a webform to a system (e.g.,mark to); (2) in response to passage of an expected data retention time,test the system by calling into the system after the passage of the dataretention time to search for the unique token. In response to findingthe unique token, the system may be configured to determine that thedata has not been properly deleted.

The system may provide a communication to the entity that includes aunique identifier associated with the data subject, is performed withoutusing a personal communication data platform, prompts the entity toprovide a response by contacting the data subject via a personalcommunication data platform. In response to providing the communicationto the entity, the system may determine whether the data subject hasreceived a response via the personal communication data platform. Thesystem may, in response to determining that the data subject hasreceived the response via the personal communication data platform,determine that the one or more computers systems have not complied withthe data subject's request for deletion of their personal data. Inresponse, the system may generate an indication that the one or morecomputers systems have not complied with the data subject's request fordeletion of their personal data by the entity, and digitally store theindication that the one or more computers systems have not complied withthe data subject's request for deletion of their personal data incomputer memory.

Automatic Preparation for Remediation

In particular embodiments, a Risk Remediation System is configured tosubstantially automatically determine whether to take one or moreactions in response to one or more identified risk triggers. Forexample, an identified risk trigger may be that a data asset for anorganization is hosted in only one particular location therebyincreasing the scope of risk if the location were infiltrated (e.g., viacybercrime). In particular embodiments, the system is configured tosubstantially automatically perform one or more steps related to theanalysis of and response to the one or more potential risk triggersdiscussed above. For example, the system may substantially automaticallydetermine a relevance of a risk posed by (e.g., a risk level) the one ormore potential risk triggers based at least in part on one or morepreviously-determined responses to similar risk triggers. This mayinclude, for example, one or more previously determined responses forthe particular entity that has identified the current risk trigger, oneor more similarly situated entities, or any other suitable entity orpotential trigger.

Various processes performed by the Risk Remediation System may beimplemented by a Data Risk Remediation Module 5200. Referring to FIG.52, in particular embodiments, the system, when executing the Data RiskRemediation Module 5200, is configured to access risk remediation datafor an entity that identifies one or more actions to remediate a risk inresponse to identifying one or more data assets of the entitypotentially affected by one or more risk triggers, receive an indicationof an update to the one or more data assets, identify one or moreupdated risk triggers for an entity based at least in part on the updateto the one or more data assets, determine, by using one or more datamodels associated with the risk remediation data, one or more updatedactions to remediate the one or more updated risk triggers, analyze theone or more updated risk triggers to determine a relevance of the riskposed to the entity by the one or more updated risk triggers, and updatethe risk remediation data to include the one or more updated actions toremediate the risk in response to identifying the one or more updatedrisk triggers.

When executing the Data Risk Remediation Module 5200, the system begins,at Step 5210, by accessing risk remediation data for an entity thatidentifies one or more actions to remediate a risk in response toidentifying one or more data assets of the entity potentially affectedby one or more risk triggers. The particular entity may include, forexample, a particular organization, company, sub-organization, etc. Theone or more data assets may include personal data for clients orcustomers. In embodiment described herein, personal data may include,for example: (1) the name of a particular data subject (which may be aparticular individual); (2) the data subject's address; (3) the datasubject's telephone number; (4) the data subject's e-mail address; (5)the data subject's social security number; (6) information associatedwith one or more of the data subject's credit accounts (e.g., creditcard numbers); (7) banking information for the data subject; (8)location data for the data subject (e.g., their present or pastlocation); (9) internet search history for the data subject; and/or (10)any other suitable personal information, such as other personalinformation discussed herein.

In some implementations, the system may include risk remediation dataassociated with one or more data assets. The risk remediation data maybe default or pre-configured risk remediation data that identifies oneor more actions to remediate a risk in response to identifying one ormore data assets of the entity potentially affected by one or more risktriggers. In some implementations, the system may have previouslyupdated and/or continuously update the risk remediation data. The riskremediation data may be updated and/or based on aggregate riskremediation data for a plurality of identified risk triggers from one ormore organizations, which may include the entity.

The system may analyze the aggregate risk remediation data to determinea remediation outcome for each of the plurality of identified risktriggers and an associated entity response to the particular identifiedrisk trigger of the plurality of identified risk triggers. Theremediation outcome is an indication of how well the entity responseaddressed the identified risk trigger. For example, the remediationoutcome can be a numerical (e.g., 1 to 10), an indication of the risktrigger after the entity response was performed (e.g., “high,” “medium,”or “low”). In response to analyzing the aggregate risk remediation datato determine a remediation outcome for each of the plurality ofidentified risk triggers and an associated entity response to theparticular identified risk trigger of the plurality of identified risktriggers, generating the data model of the one or more data models.

One or more data models for the system may be generated to indicate arecommended entity response based on each identified risk trigger. Theone or more risk remediation models base be generated in response toanalyzing the aggregate risk remediation data to determine a remediationoutcome for each of the plurality of identified risk triggers and anassociated entity response to the particular identified risk trigger ofthe plurality of identified risk triggers. Additionally, the riskremediation data for the entity may include the one or more riskremediation data models with an associated one or more data assets ofthe entity.

Continuing to Step 5220, the system is configured to receive anindication of an update to the one or more data assets. In particularembodiments, the system may indicate that a modification has beenperformed to the one or more data assets. In various embodiments, when aprivacy campaign, processing activity, etc. of the particularorganization is modified (e.g., add, remove, or update particularinformation), then the system may the risk remediation data for use infacilitating an automatic assessment of and/or response to futureidentified risk triggers. The modification may be an addition (e.g.,additional data stored to the one or more data assets), a deletion(e.g., removing data stored to the one or more data assets), or a change(e.g., editing particular data or rearranging a configuration of thedata associated with the one or more data assets. At Step 5230, thesystem is configured to identify one or more updated risk triggers foran entity based at least in part on the update to the one or more dataassets. The updated risk triggers may be anything that exposes the oneor more data assets of the entity to, for example, a data breach or aloss of data, among others. For example, an identified risk trigger maybe that a data asset for an organization is hosted in only oneparticular location thereby increasing the scope of risk if the locationwere infiltrated (e.g., via cybercrime).

At Step 5240, the system is configured to determine, by using one ormore data models associated with the risk remediation data, one or moreupdated actions to remediate the one or more updated risk triggers. Aspreviously described above, the one or more data models for the systemmay be generated to indicate a recommended entity response based on eachidentified risk trigger. The one or more risk remediation models base begenerated in response to analyzing the aggregate risk remediation datato determine a remediation outcome for each of the plurality ofidentified risk triggers and an associated entity response to theparticular identified risk trigger of the plurality of identified risktriggers.

At Step 5250, the system is configured to analyze the one or moreupdated risk triggers to determine a relevance of the risk posed to theentity by the one or more updated risk triggers. In particularembodiments, the system is configured to substantially automaticallyperform one or more steps related to the analysis of and response to theone or more potential risk triggers discussed above. For example, thesystem may substantially automatically determine a relevance of a riskposed by (e.g., a risk level) the one or more potential risk triggersbased at least in part on one or more previously-determined responses tosimilar risk triggers. This may include, for example, one or morepreviously determined responses for the particular entity that hasidentified the current risk trigger, one or more similarly situatedentities, or any other suitable entity or potential trigger. In someembodiments, the system is configured to determine, based at least inpart on the one or more data assets and the relevance of the risk,whether to take one or more updated actions in response to the one ormore updated risk triggers, and take the one or more updated actions toremediate the risk in response to identifying the one or more updatedrisk triggers.

Additionally, in some implementations, the system may calculate a risklevel based at least in part on the one or more updated risk triggers.The risk level may be compared to a threshold risk level for the entity.The threshold risk level may be pre-determined, or the entity may beable to adjust the threshold risk level (e.g., based on the type of datastored in the particular data asset, a number of data assets involved,etc.). In response to determining that the risk level is greater than orequal to the threshold risk level (i.e., a risk level that is defined asriskier than the threshold risk level or as risky as the threshold risklevel), updating the risk remediation data to include the one or moreupdated actions to remediate the risk in response to identifying the oneor more updated risk triggers. The risk level may be, for example, anumerical value (e.g., 1 to 10) or a described value (e.g., “low,”“medium,” or “high”), among others. In some implementations, calculatingthe risk level may be based at least in part on the one or more updatedrisk triggers further comprises comparing the one or more updated risktriggers to (i) one or more previously identified risk triggers, and(ii) one or more previously implemented actions to the one or morepreviously identified risk triggers.

At Step 5260, the system continues by updating the risk remediation datato include the one or more updated actions to remediate the risk inresponse to identifying the one or more updated risk triggers. Invarious embodiments, the system may automatically (e.g., substantiallyautomatically) update the risk remediation data.

In various embodiments, the system may identify one or more risktriggers for an entity based at least in part on the update to the firstdata asset of the entity, and in turn, identify a second data asset ofthe entity potentially affected by the one or more risk triggers basedat least in part on an association of a first data asset and the seconddata asset. The system may then determine, by using one or more datamodels, one or more first updated actions to remediate the one or moreupdated risk triggers for the first data asset, and determine, by usingone or more data models, one or more second updated actions to remediatethe one or more updated risk triggers for the second data asset. In someimplementations, the one or more first updated actions to remediate theone or more updated risk triggers for the first data asset may be thesame as or different from one or more second updated actions toremediate the one or more updated risk triggers for the second dataasset. Further, the system may generate (or update) risk remediationdata of the entity to include the one or more first updated actions andthe one or more second updated actions to remediate the one or morepotential risk triggers.

Central Consent Repository Maintenance and Data Inventory Linking

In particular embodiments, a Central Consent System is configured toprovide a third-party data repository system to facilitate the receiptand centralized storage of personal data for each of a plurality ofrespective data subjects, as described herein. Additionally, the CentralConsent System is configured to interface with a centralized consentreceipt management system.

Various processes performed by the Central Consent System may beimplemented by a Central Consent Module 5300. Referring to FIG. 53, inparticular embodiments, the system, when executing the Central ConsentModule 5300, is configured to: identify a form used to collect one ormore pieces of personal data, determine a data asset of a plurality ofdata assets of the organization where input data of the form istransmitted, add the data asset to the third-party data repository withan electronic link to the form in response to a user submitting theform, create a unique subject identifier associated with the user,transmit the unique subject identifier (i) to the third-party datarepository and (ii) along with the form data provided by the user in theform, to the data asset, and digitally store the unique subjectidentifier (i) in the third-party data repository and (ii) along withthe form data provided by the user in the form, in the data asset.

When executing the Central Consent Module 5300, the system begins, atStep 5310, by identifying a form used to collect one or more pieces ofpersonal data. The particular entity may include, for example, aparticular organization, company, sub-organization, etc. In particularembodiments, the one or more data assets (e.g., data systems) mayinclude, for example, any processor or database that collects,processes, contains, and/or transfers data (e.g., such as a softwareapplication, “internet of things” computerized device, database,website, data-center, server, etc.). The one or more forms may ask forpersonal data, and the one or more data assets may store personal datafor clients or customers. In embodiment described herein, personal datamay include, for example: (1) the name of a particular data subject(which may be a particular individual); (2) the data subject's address;(3) the data subject's telephone number; (4) the data subject's e-mailaddress; (5) the data subject's social security number; (6) informationassociated with one or more of the data subject's credit accounts (e.g.,credit card numbers); (7) banking information for the data subject; (8)location data for the data subject (e.g., their present or pastlocation); (9) internet search history for the data subject; and/or (10)any other suitable personal information, such as other personalinformation discussed herein.

In particular embodiments, the system is configured to identify a formvia one or more method that may include one or more website scanningtools (e.g., web crawling). The system may also receive an indicationthat a user is completing a form (e.g., a webform via a website)associated with the particular organization (e.g., a form to completefor a particular privacy campaign).

The form may include, for example, one or more fields that include theuser's e-mail address, billing address, shipping address, and paymentinformation for the purposes of collected payment data to complete acheckout process on an e-commerce website. The system may, for example,be configured to track data on behalf of an entity that collects and/orprocesses personal data related to: (1) who consented to the processingor collection of personal data (e.g., the data subject themselves or aperson legally entitled to consent on their behalf such as a parent,guardian, etc.); (2) when the consent was given (e.g., a date and time);(3) what information was provided to the consenter at the time ofconsent (e.g., a privacy policy, what personal data would be collectedfollowing the provision of the consent, for what purpose that personaldata would be collected, etc.); (4) how consent was received (e.g., oneor more copies of a data capture form, webform, etc. via which consentwas provided by the consenter); (5) when consent was withdrawn (e.g., adate and time of consent withdrawal if the consenter withdraws consent);and/or (6) any other suitable data related to receipt or withdrawal ofconsent.

Continuing to Step 5320, the system is configured to determine one ormore data assets of a plurality of data assets of the organization whereinput data of the form is transmitted. In particular embodiments, thesystem may determine one or more data assets of the organization thatreceive the form data provided by the user in the form (e.g., webform).In particular embodiments, the system is configured to identify the oneor more data assets using one or more data modeling techniques. Asdiscussed more fully above, a data model may store the followinginformation: (1) the entity that owns and/or uses a particular dataasset (e.g., such as a primary data asset, an example of which is shownin the center of the data model in FIG. 4); (2) one or more departmentswithin the organization that are responsible for the data asset; (3) oneor more software applications that collect data (e.g., personal data)for storage in and/or use by the data asset; (4) one or more particulardata subjects (or categories of data subjects) that information iscollected from for use by the data asset; (5) one or more particulartypes of data that are collected by each of the particular applicationsfor storage in and/or use by the data asset; (6) one or more individuals(e.g., particular individuals or types of individuals) that arepermitted to access and/or use the data stored in, or used by, the dataasset; (7) which particular types of data each of those individuals areallowed to access and use; and (8) one or more data assets (destinationassets) that the data is transferred to for other use, and whichparticular data is transferred to each of those data assets.

As may be understood in light of this disclosure, the system may utilizea data model (e.g., or one or more data models) to identify the one ormore data assets associated with the particular entity that receiveand/or store particular form data.

At Step 5330, the system is configured to add the one or more dataassets to the third-party data repository with an electronic link to theform. In particular embodiments, a third-party data repository systemmay electronically link the form to the one or more data assets thatprocessor or store the form data of the form. Next, at Step 5340, inresponse to a user submitting the form, the system is configured tocreate a unique subject identifier associated with the user. The systemis configured to generate, for each data subject that completes the form(e.g., a webform), a unique identifier. The system may, for example: (1)receive an indication that the form has been completed with the formincluding a piece of personal data; (2) identify a data subjectassociated with the piece of personal data; (3) determine whether thecentral repository system is currently storing data associated with thedata subject; and (4) in response to determining that one or more dataassets of the plurality of data assets is not currently storing dataassociated with the data subject (e.g., because the data subject is anew data subject), generate the unique identifier.

In particular embodiments, the unique identifier may include any uniqueidentifier such as, for example: (1) any of the one or more pieces ofpersonal data collected, stored, and/or processed by the system (e.g.,name, first name, last name, full name, address, phone number, e-mailaddress, etc.); (2) a unique string or hash comprising any suitablenumber of numerals, letters, or combination thereof; and/or (3) anyother identifier that is sufficiently unique to distinguish between afirst and second data subject for the purpose of subsequent dataretrieval. In particular embodiments, the system is configured to assigna permanent identifier to each particular data subject. In otherembodiments, the system is configured to assign one or more temporaryunique identifiers to the same data subject.

In particular embodiments, the system is configured to: (1) receive anindication of completion of a form associated with the organization by adata subject; (2) determine, based at least in part on searching aunique subject identifier database (e.g., a third-party datarepository), whether a unique subject identifier has been generated forthe data subject; (3) in response to determining that a unique subjectidentifier has been generated for the data subject, accessing the uniquesubject identifier database; (4) identify the unique subject identifierof the data subject based at least in part on form data provided by thedata subject in the completion of the form associated with theorganization; and (5) update the unique subject identifier database toinclude an electronic link between the unique subject identifier of thedata subject with each of (i) the form (e.g., including the form data)submitted by the data subject of each respective unique subjectidentifier, and (ii) one or more data assets that utilize the form dataof the form received from the data subject. In this way, as an entitycollects additional data for a particular unique data subject (e.g.,having a unique subject identifier, hash, etc.), the third party datarepository system is configured to maintain a centralized database ofdata collected, stored, and or processed for each unique data subject(e.g., indexed by unique subject identifier). The system may then, inresponse to receiving a data subject access request from a particulardata subject, fulfill the request substantially automatically (e.g., byproviding a copy of the personal data, deleting the personal data,indicating to the entity what personal data needs to be deleted fromtheir system and where it is located, etc.). The system may, forexample, automatically fulfill the request by: (1) identifying theunique subject identifier associated with the unique data subject makingthe request; and (2) retrieving any information associated with theunique data subject based on the unique subject identifier.

Continuing to Step 5350, the system is configured to transmit the uniquesubject identifier (i) to the third-party data repository and (ii) alongwith the form data provided by the user in the form, to the data asset.At Step 5360, the system is configured to digitally store the uniquesubject identifier (i) in the third-party data repository and (ii) alongwith the form data provided by the user in the form, in the data asset.As may understood in light of this disclosure, the system may then beconfigured to facilitate the receipt and centralized storage of personaldata for each of a plurality of respective data subjects and theassociated one or more data assets that process or store the form dataprovided by the data subject.

In particular embodiments, the system may be further configured forreceiving a data subject access request from the user, accessing thethird-party data repository to identify the unique subject identifier ofthe user, determining which one or more data assets of the plurality ofdata assets of the organization include the unique subject identifier,and accessing personal data (e.g., form data) of the user stored in eachof the one or more data assets of the plurality of data assets of theorganization that include the unique subject identifier. In particularembodiments, the data subject access request may be a subject's rightsrequest where the data subject may be inquiring for the organization toprovide all data that the particular organization has obtained on thedata subject or a data subject deletion request where the data subjectis requesting for the particular organization to delete all data thatthe particular organization has obtained on the data subject.

In particular embodiments, when the data subject access request is adata subject deletion request, in response to accessing the personaldata of the user stored in each of the one or more data assets of theplurality of data assets of the organization that include the uniquesubject identifier, the system deletes the personal data of the userstored in each of the one or more data assets of the plurality of dataassets of the organization that include the unique subject identifier.In some embodiments, when the data subject access request is a datasubject deletion request, the system may be configured to: (1) inresponse to accessing the personal data of the user stored in each ofthe one or more data assets of the plurality of data assets,automatically determine that a first portion of personal data of theuser stored in the one or more data assets has one or more legal basesfor continued storage; (2) in response to determining that the firstportion of personal data of the user stored in the one or more dataassets has one or more legal bases for continued storage, automaticallymaintain storage of the first portion of personal data of the userstored in the one or more data assets; (3) in response to determiningthat the first portion of personal data of the user stored in the one ormore data assets has one or more legal bases for continued storage,automatically maintaining storage of the first portion of personal dataof the user stored in the one or more data assets; and (4) automaticallyfacilitating deletion of a second portion of personal data of the userstored in the one or more data assets for which one or more legal basesfor continued storage cannot be determined, wherein the first portion ofthe personal data of the user stored in the one or more data assets isdifferent from the second portion of personal data of the user stored inthe one or more data assets.

Data Transfer Risk Identification and Analysis

In particular embodiments, a Data Transfer Risk Identification System isconfigured to analyze one or more data systems (e.g., data assets),identify data transfers between/among those systems, apply data transferrules to each data transfer record, perform a data transfer assessmenton each data transfer record based on the data transfer rules to beapplied to each data transfer record, and calculate a risk score for thedata transfer based at least in part on the one or more data transferrisks associated with the data transfer record.

Various processes performed by the Data Transfer Risk IdentificationSystem may be implemented by Data Transfer Risk Identification Module5400. Referring to FIG. 54, in particular embodiments, the system, whenexecuting the Data Transfer Risk Identification Module 5400, isconfigured for: (1) creating a data transfer record for a data transferbetween a first asset in a first location and a second asset in a secondlocation; (2) accessing a set of data transfer rules that are associatedwith the data transfer record; (3) performing a data transfer assessmentbased at least in part on applying the set of data transfer rules on thedata transfer record; (4) identifying one or more data transfer risksassociated with the data transfer record, based at least in part on thedata transfer assessment; (5) calculating a risk score for the datatransfer based at least in part on the one or more data transfer risksassociated with the data transfer record; and (6) digitally storing therisk score for the data transfer.

When executing the Data Transfer Risk Identification Module 5400, thesystem begins, at Step 5410, by creating a data transfer record for adata transfer between a first asset in a first location and a secondasset in a second location. The data transfer record may be created foreach transfer of data between a first asset in a first location and asecond asset in a second location where the transfer record may alsoinclude information regarding the type of data being transferred, a timeof the data transfer, an amount of data being transferred, etc. In someembodiments, the system may access a data transfer record that may havealready been created by the system.

In various embodiments, the system may be configured to determine inwhich of the one or more defined plurality of physical locations eachparticular data system is physically located. In particular embodiments,the system is configured to determine the physical location based atleast in part on one or more data attributes of a particular data asset(e.g., data system) using one or more data modeling techniques (e.g.,using one or more suitable data modeling techniques described herein).In some embodiments, the system may be configured to determine thephysical location of each data asset based at least in part on anexisting data model that includes the data asset. In still otherembodiments, the system may be configured to determine the physicallocation based at least in part on an IP address and/or domain of thedata asset (e.g., in the case of a computer server or other computingdevice) or any other identifying feature of a particular data asset.

In particular embodiments, the system is configured to identify one ormore data elements stored by the one or more data systems that aresubject to transfer (e.g., transfer to the one or more data systems suchas from a source asset, transfer from the one or more data systems to adestination asset, etc.). In particular embodiments, the system isconfigured to identify a particular data element that is subject to suchtransfer (e.g., such as a particular piece of personal data or otherdata). In some embodiments, the system may be configured to identify anysuitable data element that is subject to transfer and includes personaldata.

In any embodiment described herein, personal data may include, forexample: (1) the name of a particular data subject (which may be aparticular individual); (2) the data subject's address; (3) the datasubject's telephone number; (4) the data subject's e-mail address; (5)the data subject's social security number; (6) information associatedwith one or more of the data subject's credit accounts (e.g., creditcard numbers); (7) banking information for the data subject; (8)location data for the data subject (e.g., their present or pastlocation); (9) internet search history for the data subject; and/or (10)any other suitable personal information, such as other personalinformation discussed herein.

In some embodiments, with regard to the location of the one or more dataassets, the system may define a geographic location of the one or moredata assets. For example, define each of the plurality of physicallocations based at least in part on one or more geographic boundaries.These one or more geographic boundaries may include, for example: (1)one or more countries; (2) one or more continents; (3) one or morejurisdictions (e.g., such as one or more legal jurisdictions); (4) oneor more territories; (5) one or more counties; (6) one or more cities;(7) one or more treaty members (e.g., such as members of a trade,defense, or other treaty); and/or (8) any other suitable geographicallydistinct physical locations.

Continuing to Step 5420, the system is configured for accessing a set ofdata transfer rules that are associated with the data transfer record.The system may apply data transfer rules to each data transfer record.The data transfer rules may be configurable to support different privacyframeworks (e.g., a particular data subject type is being transferredfrom a first asset in the European Union to a second asset outside ofthe European Union) and organizational frameworks (e.g., to support thedifferent locations and types of data assets within an organization).The applied data transfer rules may be automatically configured by thesystem (e.g., when an update is applied to privacy rules in a country orregion) or manually adjusted by the particular organization (e.g., by aprivacy officer of the organization). The data transfer rules to beapplied may vary based on the data being transferred.

As may be understood from this disclosure, the transfer of personal datamay trigger one or more regulations that govern such transfer. Inparticular embodiments, personal data may include any data which relateto a living individual who can be identified: (1) from the data; or (2)from the data in combination with other information which is in thepossession of, or is likely to come into the possession of a particularentity. In particular embodiments, a particular entity may collect,store, process, and/or transfer personal data for one or more customers,one or more employees, etc.

In various embodiments, the system is configured to use one or more datamodels of the one or more data assets (e.g., data systems) to analyzeone or more data elements associated with those assets to determinewhether the one or more data elements include one or more data elementsthat include personal data and are subject to transfer. In particularembodiments, the transfer may include, for example: (1) an internaltransfer (e.g., a transfer from a first data asset associated with theentity to a second data asset associated with the entity); (2) anexternal transfer (e.g., a transfer from a data asset associated withthe entity to a second data asset associated with a second entity);and/or (3) a collective transfer (e.g., a transfer to a data assetassociated with the entity from an external data asset associated with asecond entity).

The particular entity may include, for example, a particularorganization, company, sub-organization, etc. In particular embodiments,the one or more data assets (e.g., data systems) may include, forexample, any entity that collects, processes, contains, and/or transfersdata (e.g., such as a software application, “internet of things”computerized device, database, web site, data-center, server, etc.). Forexample, a first data asset may include any software or device utilizedby a particular entity for such data collection, processing, transfer,storage, etc. In various embodiments, the first data asset may be atleast partially stored on and/or physically located in a particularlocation. For example, a server may be located in a particular country,jurisdiction, etc. A piece of software may be stored on one or moreservers in a particular location, etc.

In particular embodiments, the system is configured to identify the oneor more data systems using one or more data modeling techniques. Asdiscussed more fully above, a data model may store the followinginformation: (1) the entity that owns and/or uses a particular dataasset (e.g., such as a primary data asset, an example of which is shownin the center of the data model in FIG. 4); (2) one or more departmentswithin the organization that are responsible for the data asset; (3) oneor more software applications that collect data (e.g., personal data)for storage in and/or use by the data asset; (4) one or more particulardata subjects (or categories of data subjects) that information iscollected from for use by the data asset; (5) one or more particulartypes of data that are collected by each of the particular applicationsfor storage in and/or use by the data asset; (6) one or more individuals(e.g., particular individuals or types of individuals) that arepermitted to access and/or use the data stored in, or used by, the dataasset; (7) which particular types of data each of those individuals areallowed to access and use; and (8) one or more data assets (destinationassets) that the data is transferred to for other use, and whichparticular data is transferred to each of those data assets.

As may be understood in light of this disclosure, the system may utilizea data model (e.g., or one or more data models) of data assetsassociated with a particular entity to identify the one or more datasystems associated with the particular entity.

Next, at Step 5430, the system is configured for performing a datatransfer assessment based at least in part on applying the set of datatransfer rules on the data transfer record. The data transfer assessmentperformed by the system may identify risks associated with the datatransfer record. At Step 5440, the system is configured for identifyingone or more data transfer risks associated with the data transferrecord, based at least in part on the data transfer assessment. The oneor more data transfer risks may include, for example, a source locationof the first location of the one or more first data asset of the datatransfer, a destination location of the second location of the one ormore second data asset of the data transfer, one or more type of databeing transferred as part of the data transfer (e.g., personal data orsensitive data), a time of the data transfer (e.g., date, day of theweek, time, month, etc.), an amount of data being transferred as part ofthe data transfer.

Continuing to Step 5450, the system is configured for calculating a riskscore for the data transfer based at least in part on the one or moredata transfer risks associated with the data transfer record. The riskscore may be calculated in a multitude of ways, and may include one ormore data transfer risks such as a source location of the data transfer,a destination location of the data transfer, the type of data beingtransferred, a time of the data transfer, an amount of data beingtransferred, etc. Additionally, the system may apply weighting factors(e.g., manually or automatically determined) to the risk factors.Further, in some implementations, the system may include a thresholdrisk score where a data transfer may be terminated if the data transferrisk score indicates a higher risk than the threshold risk score (e.g.,the data transfer risk score being higher than the threshold riskscore).

In some embodiments, the system may compare the risk score for the datatransfer to a threshold risk score, determine that the risk score forthe data transfer is a greater risk than the threshold risk score, andin response to determining that the risk score for the data transfer isa greater risk than the threshold risk score, taking one or more action.The one or more action may include, for example, provide the datatransfer record to one or more individuals (e.g., a privacy officer) forreview of the data transfer record where the one or more individuals maymake a decision to approve the data transfer or terminate the datatransfer. In some implementations, the system may automaticallyterminate the data transfer.

In some implementations, the system may generate a secure link betweenone or more processors associated with the first asset in the firstlocation and one or more processors associated with the second asset inthe second location, and the system may further provide the datatransfer via the secure link between the one or more processorsassociated with the first asset in the first location and the one ormore processors associated with the second asset in the second location.

In various embodiments, the system may determine a weighting factor foreach of the one or more data transfer risks, determine a risk rating foreach of the one or more data transfer risks, and calculate the risklevel for the data transfer based upon, for each respective one of theone or more data transfer risks, the risk rating for the respective datatransfer risk and the weighting factor for the respective data transferrisk.

At Step 5460, the system continues by digitally storing the risk scorefor the data transfer. In various embodiments, the system may continueby transferring the data between the first asset in the first locationand the second asset in the second location. In some embodiments, thesystem may be configured to substantially automatically flag aparticular transfer of data as problematic (e.g., because the transferdoes not comply with an applicable regulation). For example, aparticular regulation may require data transfers from a first asset to asecond asset to be encrypted.

Automated Classification of Personal Information from Documents

In any embodiment described herein, an automated classification systemmay be configured to substantially automatically classify one or morepieces of personal information in one or more documents (e.g., one ormore text-based documents, one or more spreadsheets, one or more PDFs,one or more webpages, etc.). In particular embodiments, the system maybe implemented in the context of any suitable privacy compliance system,which may, for example, be configured to calculate and assign asensitivity score to a particular document based at least in part on oneor more determined categories of personal information (e.g., personaldata) identified in the one or more documents. As understood in the art,the storage of particular types of personal information may be governedby one or more government or industry regulations. As such, it may bedesirable to implement one or more automated measures to automaticallyclassify personal information from stored documents (e.g., to determinewhether such documents may require particular security measures, storagetechniques, handling, whether the documents should be destroyed, etc.).

FIG. 55 is a flowchart of process steps that the system may perform inthe automatic classification of personal information in an electronicdocument. When executing the Automated Classification Module 5500, thesystem begins, at Step 5510, by receiving and/or retrieving one or moreelectronic documents for analysis and classification. The system may,for example, receive a particular document from a user for analysis. Inother embodiments, the system may be configured to automatically scanelectronic documents stored on a system (e.g., on one or more servers,in one or more databases, or in any other suitable location) to classifyany personal information that may be stored therein. In variousembodiments, the one or more electronic documents may include, forexample: (1) one or more PDFs; (2) one or more spreadsheets; (3) one ormore text-based documents; (4) one or more audio files; (5) one or morevideo files; (6) one or more webpages; and/or (7) any other suitabletype of document.

FIG. 56 depicts an exemplary electronic document that the system mayreceive and/or retrieve for analysis. As may be understood from FIG. 56(e.g., a PDF or other text-based document), the electronic documentcontains employee information such as: (1) first name; (2) last name;(3) social security number; (3) address; (4) marital status; (5) phonenumber; (6) employer information; (7) etc.

Continuing to Step 5520, the system is configured to use one or morenatural language processing techniques to determine data from the one ormore electronic documents into one or more structured objects. Thesystem may, for example, use one or more optical character recognition(OCR) techniques to identify particular text in the electronicdocuments. In some embodiments, the system may be configured to use oneor more audio processing techniques to identify one or more words in anaudio recording, etc.

The system, in particular embodiments, may be configured to: (1) parsethe document to identify context for particular identified text (e.g.,identify context based at least in part on proximity to other identifiedtext, etc.); (2) parse out labels from the document; and (3) parse outvalues for the various labels. The system may, for example, identifyparticular categories of information contained in document. As may beunderstood from FIG. 3, the system may be configured to identifyparticular labels such as, for example: (1) first name; (2) last name;(3) city; and (4) so on. The system may be further configured toidentify values associated with each label such as: (1) DOE for lastname; (2) JOHN for first name; (3) etc. The system may be configured todetermine these values based on, for example: (1) a proximity of thevalues to the labels; (2) a position of the values relative to thelabels; (3) one or more natural language processing techniques (e.g.,the system may be configured to identify John as a name, and thenassociate John with the identified label for name, etc.). The system maythen be further configured to electronically associate the identifiedvalues with their respective labels (e.g., in computer memory).

In any embodiment described herein, the system may then generate aclassification of one or more structured objects identified using thenatural language processing techniques described above. For example, thesystem may be configured to generate a catalog of labels identified inthe electronic document. FIG. 57 depicts an illustration of one or moreobject that the system has generated based on the document shown in FIG.56 as a result of the scanning described above.

Continuing to Step 5530, the system is configured to classify each ofthe one or more structured objects based on one or more attributes ofthe structured objects. For example, the system may be configured to usecontextual information, sentiment, and/or syntax to classify each of thestructured objects. FIG. 58 depicts an exemplary classification of thestructured objects cataloged from FIG. 57. As may be understood fromthis Figure, the system may be configured to group objects based in parton a type of information. For example, the various objects related to anindividual's name (e.g., first name, last name, etc.) may be groupedinto a single classification. The system may, for example, be configuredto automatically classify the one or more objects based on: (1) theobject's proximity in the particular document; (2) one or more headingsidentified in the document; and/or (3) any other suitable factor. Forexample, in various embodiments, the system is configured to use one ormore machine learning and/or natural language techniques to identify arelation between objects.

The system may then be configured to identify one or more objectswithout associated values and remove those objects from theclassification. FIGS. 59-60 depict a visual representation of objectswithout associated values from the PDF shown in FIG. 56 being blackedout and removed from the classification. The system may, for example, beconfigured to generate an initial classification based on the document,and then modify the classification based on one or more identifiedvalues in the specific document.

Continuing to Step 5540, the system is configured to categorize each ofthe one or more structured objects based at least in part on asensitivity of information determined based on the one or moreattributes of the objects. The system may be configured to determine thecategorization based on sensitivity based on, for example: (1) one ormore predefined sensitivities for particular categories of information;(2) one or more user-defined sensitivities; (3) one or moresensitivities determined automatically based on one or more prevailingindustry or government regulations directed toward the type ofinformation associated with the objects; (4) etc.

FIG. 62 depicts an exemplary mapping of values and structured objectsbased on a sensitivity of the structured objects. As may be understoodfrom this figure, the system is configured to cross-reference thecategorization of structured objects with a database of personal dataclassification, which may, for example, identify a sensitivity ofparticular categories of structured objects (e.g., personallyidentifiable information, sensitive personal data, partial PII, personaldata, not personal data, etc.). The system may then be configured to mapthe results as shown in FIG. 62.

Next, at Step 5550, the system is configured to rate the accuracy of thecategorization performed at Step 5540. The system may, for example, beconfigured to rate the categorization by comparing the categorizationdetermined for a similar electronic document (e.g., a second electronicdocument that includes the same form filled out by another individualthan John Doe). In other embodiments, the system may be configured torate the accuracy of the categorization based on one or more attributes(e.g., one or more values) of the structured objects. The system may,for example, analyze the value for a particular object to determine anaccuracy of the categorization of the object. For example, an object forfirst name may be categorized as “employee information,” and the systemmay be configured to analyze a value associated with the object todetermine whether the categorization is accurate (e.g., analyze thevalue to determine whether the value is, in fact, a name). The systemmay, for example, determine that the accuracy of the categorization isrelatively low in response to determining that a value for the “firstname” object contains a number string or a word that is nottraditionally a name (e.g., such as ‘attorney’ or another job title, aphone number, etc.). The system may determine a character type (e.g.,set of numbers, letters, a combination of numbers and letters, etc.) foreach object and a character type for each value of the object todetermine the accuracy of the categorization. The character type foreach object and each value of the object may be compared to determinethe accuracy of the categorization by the system.

Continuing to Step 5560, the system is configured to generate asensitivity score for each element in the one or more electronicdocuments and each document as a whole based at least in part on thecategory and sensitivity of each object. The system may, for example,assign a relative sensitivity to the document based on each relativesensitivity score assigned to each object identified in the document.The system may, in various embodiments, calculate a sensitivity scorefor each object based at least in part on a confidence in the accuracyof the categorization of the object and the sensitivity assigned to theparticular categorization.

Conclusion

Although embodiments above are described in reference to various privacycompliance monitoring systems, it should be understood that variousaspects of the system described above may be applicable to otherprivacy-related systems, or to other types of systems, in general.

While this specification contains many specific embodiment details,these should not be construed as limitations on the scope of anyinvention or of what may be Concepted, but rather as descriptions offeatures that may be specific to particular embodiments of particularinventions. Certain features that are described in this specification inthe context of separate embodiments may also be implemented incombination in a single embodiment. Conversely, various features thatare described in the context of a single embodiment may also beimplemented in multiple embodiments separately or in any suitablesub-combination. Moreover, although features may be described above asacting in certain combinations and even initially Concepted as such, oneor more features from a Concepted combination may in some cases beexcised from the combination, and the Concepted combination may bedirected to a sub-combination or variation of a sub-combination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the embodiments described above should not be understoodas requiring such separation in all embodiments, and it should beunderstood that the described program components and systems maygenerally be integrated together in a single software product orpackaged into multiple software products.

Many modifications and other embodiments of the invention will come tomind to one skilled in the art to which this invention pertains havingthe benefit of the teachings presented in the foregoing descriptions andthe associated drawings. Therefore, it is to be understood that theinvention is not to be limited to the specific embodiments disclosed andthat modifications and other embodiments are intended to be includedwithin the scope of the appended Concepts. Although specific terms areemployed herein, they are used in a generic and descriptive sense onlyand not for the purposes of limitation.

Although embodiments above are described in reference to various datasubject access fulfillment systems, it should be understood that variousaspects of the system described above may be applicable to otherprivacy-related systems, or to other types of systems, in general.

While this specification contains many specific embodiment details,these should not be construed as limitations on the scope of anyinvention or of what may be Concepted, but rather as descriptions offeatures that may be specific to particular embodiments of particularinventions. Certain features that are described in this specification inthe context of separate embodiments may also be implemented incombination in a single embodiment. Conversely, various features thatare described in the context of a single embodiment may also beimplemented in multiple embodiments separately or in any suitablesub-combination. Moreover, although features may be described above asacting in certain combinations and even initially Concepted as such, oneor more features from a Concepted combination may in some cases beexcised from the combination, and the Concepted combination may bedirected to a sub-combination or variation of a sub-combination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the embodiments described above should not be understoodas requiring such separation in all embodiments, and it should beunderstood that the described program components and systems maygenerally be integrated together in a single software product orpackaged into multiple software products. In addition, it should beunderstood that terms such as “in some embodiments”, “in variousembodiments”, and “in certain embodiments” are intended to indicate thatthe stated features may be implemented in any suitable embodimentdescribed herein.

Many modifications and other embodiments of the invention will come tomind to one skilled in the art to which this invention pertains havingthe benefit of the teachings presented in the foregoing descriptions andthe associated drawings. Therefore, it is to be understood that theinvention is not to be limited to the specific embodiments disclosed andthat modifications and other embodiments are intended to be includedwithin the scope of the appended Concepts. Although specific terms areemployed herein, they are used in a generic and descriptive sense onlyand not for the purposes of limitation.

What is claimed is:
 1. A computer-implemented data processing method foridentifying one or more pieces of personal data that are not associatedwith one or more privacy campaigns of a particular entity, the methodcomprising: accessing, by one or more processors, via one or morecomputer networks, one or more data assets of the particular entity;scanning, by one or more processors, the one or more data assets togenerate a catalog of one or more privacy campaigns and one or morepieces of personal information associated with one or more individuals;storing, by one or more processors, the generated catalog in computermemory; scanning, by one or more processors, one or more data assetsbased at least in part on the generated catalog to identify a firstportion of the one or more pieces of personal data that are one or morepieces of personal data that are not associated with the one or moreprivacy campaigns; generating, by one or more processors, an indicationthat the first portion of one or more pieces of personal data that arenot associated with the one or more privacy campaigns of the particularentity is to be removed from the one or more data assets; presenting, byone or more processors, the indication to one or more individualsassociated with the particular entity; and removing, by one or moreprocessors, the first portion of the one or more pieces of personal datathat are not associated with the one or more privacy campaigns of theparticular entity from the one or more data assets.
 2. Thecomputer-implemented data processing method of claim 1, wherein thefirst portion of the one or more pieces of personal data that are notassociated with the one or more privacy campaigns of the particularentity are automatically removed from the one or more data assets. 3.The computer-implemented data processing method of claim 1, furthercomprising: determining that one or more privacy campaigns have beenterminated within the one or more data assets of the particular entity;scanning the one or more data assets based at least in part on thegenerated catalog to identify the one or more pieces of personal datathat are associated with the terminated one or more privacy campaigns;and generating an indication that the one or more pieces of personaldata that are associated with the terminated one or more privacycampaigns are included in the first portion of the one or more pieces ofpersonal data.
 4. The computer-implemented data processing method ofclaim 3, further comprising: determining that one or more privacycampaigns of the particular entity have not been utilized in a period oftime; and terminating the one or more privacy campaigns of theparticular entity that have not been utilized in the period of time. 5.The computer-implemented data processing method of claim 4, wherein theperiod of time is ninety or more days.
 6. The computer-implemented dataprocessing method of claim 1, wherein presenting the indication to theone or more individuals associated with the particular entity furthercomprises: receiving, by one or more processors, a selection, by the oneor more individuals associated with the particular entity, of a firstset of the one or more pieces of personal data of the first portion ofthe one or more pieces of personal data to retain based on one or morebases to retain the first set of the one or more pieces of personaldata; prompting, by one or more processors, the one or more individualsto provide one or more bases to retain the first set of the one or morepieces of personal data of the first portion of the one or more piecesof personal data that are not associated with the one or more privacycampaigns; receiving, by one or more processors, the provided one ormore bases to retain the first set of the one or more pieces of personaldata of the first portion of the one or more pieces of personal datafrom the one or more individuals associated with the particular entity;retaining, by one or more processors, the first set of the one or morepieces of personal data of the first portion of the one or more piecesof personal data from the one or more individuals associated with theparticular entity; and removing a second set of the one or more piecesof personal data of the first portion of the one or more pieces ofpersonal data that are not associated with the one or more privacycampaigns from the one or more data assets, wherein the second set ofthe one or more pieces of personal data is different from the first setof the one or more pieces of personal data and the first portion of theone or more pieces of personal data comprise the first set of the one ormore pieces of personal data and the second set of the one or morepieces of personal data.
 7. The computer-implemented data processingmethod of claim 6, further comprising: in response to receiving theprovided one or more bases to retain the first set of the one or morepieces of personal data from the one or more individuals associated withthe particular entity, submitting the provided one or more bases toretain the first set of the one or more pieces of personal data to oneor more second individuals associated with the particular entity forauthorization.
 8. The computer-implemented data processing method ofclaim 6, wherein the second set of the one or more pieces of personaldata does not include one or more pieces of personal data.
 9. Acomputer-implemented data processing method for removing one or morepieces of personal data that are not associated with one or more privacycampaigns of a particular entity, the method comprising: accessing, byone or more processors, via one or more computer networks, one or moredata models that map an association between (i) one or more pieces ofpersonal data associated with one or more individuals stored within oneor more data assets of the particular entity and (ii) one or moreprivacy campaigns of the particular entity; analyzing, by one or moreprocessors, the one or more data models to identify a first portion ofthe one or more pieces of personal data that are one or more pieces ofpersonal data that are not associated with the one or more privacycampaigns; and automatically removing the first portion of the one ormore pieces of personal data that are not associated with the one ormore privacy campaigns of the particular entity from the one or moredata assets.
 10. The computer-implemented data processing method ofclaim 9, further comprising: receiving, by one or more processors, anindication of a new privacy campaign initiated by the particular entity;in response to receiving the indication of the new privacy campaigninitiated by the particular entity, modifying the one or more datamodels to map an association between (i) one or more pieces of personaldata associated with one or more individuals obtained in connection withthe new privacy campaign and (ii) the new privacy campaign initiated bythe particular entity.
 11. The computer-implemented data processingmethod of claim 9, further comprising: generating an indication that thefirst portion of the one or more pieces of personal data that are notassociated with the one or more privacy campaigns of the particularentity is to be removed from the one or more data assets of theparticular entity; and presenting the indication to one or moreindividuals associated with the particular entity.
 12. Thecomputer-implemented data processing method of claim 9, furthercomprising: determining that one or more privacy campaigns have beenterminated within the one or more data assets of the particular entity;analyzing, by one or more processors, the one or more data models toidentify one or more pieces of personal data that are one or more piecesof personal data that are associated with the terminated one or moreprivacy campaigns; and generating an indication that the one or morepieces of personal data that are associated with the terminated one ormore privacy campaigns are included in the first portion of the one ormore pieces of personal data.
 13. The computer-implemented dataprocessing method of claim 12, further comprising: determining that oneor more privacy campaigns of the particular entity have not beenutilized in a period of time; and terminating the one or more privacycampaigns of the particular entity have not been utilized in the periodof time.
 14. The computer-implemented data processing method of claim13, wherein the period of time is ninety or more days.
 15. Acomputer-implemented data processing method for generating a privacydata report of a particular entity, the method comprising: accessing, byone or more processors, via one or more computer networks, one or moredata models that map an association between (i) one or more pieces ofpersonal information of one or more individuals stored within one ormore data assets of the particular entity and (ii) one or more privacycampaigns of the particular entity; accessing, by one or moreprocessors, a data collection policy of the particular entity that isbased at least in part on one or more collection parameters defining howone or more pieces of personal data of one or more individuals iscollected by the particular entity and one or more storage parametersassociated with storing the one or more pieces of personal data of theone or more individuals, and one or more data retention metrics of theparticular entity that are based at least in part on the collection andstorage by the particular entity of the one or more pieces of personaldata of one or more individuals; analyzing, by or more processors, theone or more data models to identify one or more pieces of personal datathat are not associated with the one or more privacy campaigns;generating, by one or more processors, a privacy data report based atleast in part on (i) analyzing the one or more data models to identifyone or more pieces of personal data that are not associated with the oneor more privacy campaigns, (ii) the data collection policy of theparticular entity, and (iii) the one or more data retention metrics ofthe particular entity; and providing, by one or more processors, theprivacy data report to one or more individuals associated with theparticular entity.
 16. The computer-implemented data processing methodof claim 15, wherein the privacy data report comprises a comparison ofthe data collection policy and the one or more data retention metrics ofthe particular entity to one or more industry standard data collectionpolicies and one or more industry standard data retention metrics. 17.The computer-implemented data processing method of claim 15, whereingenerating the privacy data report further comprises: calculating a datarisk score for the particular entity based at least in part on (i)analyzing the one or more data models to identify one or more pieces ofpersonal data that are not associated with the one or more privacycampaigns, (ii) the data collection policy of the particular entity, and(iii) the one or more data retention metrics of the particular entity.18. The computer-implemented data processing method of claim 17, furthercomprising: comparing the data risk score for the particular entity to athreshold data risk score; determining that the data risk score for theparticular entity is less than the threshold data risk score; inresponse to determining that the data risk score for the particularentity is less than the threshold risk score, generating a notificationto indicate that the data risk score for the particular entity is lessthan the threshold risk score; and providing the notification to the oneor more individuals associated with the particular entity.
 19. Thecomputer-implemented data processing method of claim 17, furthercomprising: comparing the data risk score for the particular entity to athreshold data risk score; determining that the data risk score for theparticular entity is greater than or equal to the threshold data riskscore; in response to determining that the data risk score for theparticular entity is greater than the threshold risk score, generating anotification to indicate that the data risk score for the particularentity is greater than the threshold risk score; and providing thenotification to the one or more individuals associated with theparticular entity.
 20. The computer-implemented data processing methodof claim 15, wherein the one or more data retention metrics comprise atleast one data retention metric selected from a group consisting of: astorage location of the one or more pieces of personal data; a period oftime the one or more pieces of personal data are stored by theparticular entity; a number of the one or more privacy campaignsaccessing the one or more pieces of personal data; and an amount of theone or more pieces of personal data being collected by the particularentity.